idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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4,001 | Which loss function is correct for logistic regression? | The relationship is as follows: $l(\beta) = \sum_i L(z_i)$.
Define a logistic function as $f(z) = \frac{e^{z}}{1 + e^{z}} = \frac{1}{1+e^{-z}}$. They possess the property that $f(-z) = 1-f(z)$. Or in other words:
$$
\frac{1}{1+e^{z}} = \frac{e^{-z}}{1+e^{-z}}.
$$
If you take the reciprocal of both sides, then take the... | Which loss function is correct for logistic regression? | The relationship is as follows: $l(\beta) = \sum_i L(z_i)$.
Define a logistic function as $f(z) = \frac{e^{z}}{1 + e^{z}} = \frac{1}{1+e^{-z}}$. They possess the property that $f(-z) = 1-f(z)$. Or in | Which loss function is correct for logistic regression?
The relationship is as follows: $l(\beta) = \sum_i L(z_i)$.
Define a logistic function as $f(z) = \frac{e^{z}}{1 + e^{z}} = \frac{1}{1+e^{-z}}$. They possess the property that $f(-z) = 1-f(z)$. Or in other words:
$$
\frac{1}{1+e^{z}} = \frac{e^{-z}}{1+e^{-z}}.
$$... | Which loss function is correct for logistic regression?
The relationship is as follows: $l(\beta) = \sum_i L(z_i)$.
Define a logistic function as $f(z) = \frac{e^{z}}{1 + e^{z}} = \frac{1}{1+e^{-z}}$. They possess the property that $f(-z) = 1-f(z)$. Or in |
4,002 | Which loss function is correct for logistic regression? | OP mistakenly believes the relationship between these two functions is due to the number of samples (i.e. single vs all). However, the actual difference is simply how we select our training labels.
In the case of binary classification we may assign the labels $y=\pm1$ or $y=0,1$.
As it has already been stated, the log... | Which loss function is correct for logistic regression? | OP mistakenly believes the relationship between these two functions is due to the number of samples (i.e. single vs all). However, the actual difference is simply how we select our training labels.
In | Which loss function is correct for logistic regression?
OP mistakenly believes the relationship between these two functions is due to the number of samples (i.e. single vs all). However, the actual difference is simply how we select our training labels.
In the case of binary classification we may assign the labels $y=\... | Which loss function is correct for logistic regression?
OP mistakenly believes the relationship between these two functions is due to the number of samples (i.e. single vs all). However, the actual difference is simply how we select our training labels.
In |
4,003 | Which loss function is correct for logistic regression? | I learned the loss function for logistic regression as follows.
Logistic regression performs binary classification, and so the label outputs are binary, 0 or 1. Let $P(y=1|x)$ be the probability that the binary output $y$ is 1 given the input feature vector $x$. The coefficients $w$ are the weights that the algorithm i... | Which loss function is correct for logistic regression? | I learned the loss function for logistic regression as follows.
Logistic regression performs binary classification, and so the label outputs are binary, 0 or 1. Let $P(y=1|x)$ be the probability that | Which loss function is correct for logistic regression?
I learned the loss function for logistic regression as follows.
Logistic regression performs binary classification, and so the label outputs are binary, 0 or 1. Let $P(y=1|x)$ be the probability that the binary output $y$ is 1 given the input feature vector $x$. T... | Which loss function is correct for logistic regression?
I learned the loss function for logistic regression as follows.
Logistic regression performs binary classification, and so the label outputs are binary, 0 or 1. Let $P(y=1|x)$ be the probability that |
4,004 | Which loss function is correct for logistic regression? | Instead of Mean Squared Error, we use a cost function called Cross-Entropy, also known as Log Loss. Cross-entropy loss can be divided into two separate cost functions: one for y=1 and one for y=0.
\begin{align}\newcommand{\Cost}{{\rm Cost}}\newcommand{\if}{{\rm if}}
j(\theta) &= \frac 1 m \sum_{i=1}^m \Cost(h_\theta(x^... | Which loss function is correct for logistic regression? | Instead of Mean Squared Error, we use a cost function called Cross-Entropy, also known as Log Loss. Cross-entropy loss can be divided into two separate cost functions: one for y=1 and one for y=0.
\be | Which loss function is correct for logistic regression?
Instead of Mean Squared Error, we use a cost function called Cross-Entropy, also known as Log Loss. Cross-entropy loss can be divided into two separate cost functions: one for y=1 and one for y=0.
\begin{align}\newcommand{\Cost}{{\rm Cost}}\newcommand{\if}{{\rm if... | Which loss function is correct for logistic regression?
Instead of Mean Squared Error, we use a cost function called Cross-Entropy, also known as Log Loss. Cross-entropy loss can be divided into two separate cost functions: one for y=1 and one for y=0.
\be |
4,005 | Which loss function is correct for logistic regression? | They are the same functions.
In the first one, $y_i$ is either $0$ or $1$. While in the second, $y_i$ is either $-1$ or $1$.
The second one can be derived from the first one, as the probabilities in the second function can be written as only one equation, that is, the sigmoid function of $z_i y_i$ ($z_i$ is the linear ... | Which loss function is correct for logistic regression? | They are the same functions.
In the first one, $y_i$ is either $0$ or $1$. While in the second, $y_i$ is either $-1$ or $1$.
The second one can be derived from the first one, as the probabilities in t | Which loss function is correct for logistic regression?
They are the same functions.
In the first one, $y_i$ is either $0$ or $1$. While in the second, $y_i$ is either $-1$ or $1$.
The second one can be derived from the first one, as the probabilities in the second function can be written as only one equation, that is,... | Which loss function is correct for logistic regression?
They are the same functions.
In the first one, $y_i$ is either $0$ or $1$. While in the second, $y_i$ is either $-1$ or $1$.
The second one can be derived from the first one, as the probabilities in t |
4,006 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | The first three points, as far as I can tell, are a variation on a single argument.
Scientists often treat uncertainty measurements ($12 \pm 1 $, for instance) as probability distributions that look like this:
When actually, they are much more likely to look like this:
As a former chemist, I can confirm that many s... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | The first three points, as far as I can tell, are a variation on a single argument.
Scientists often treat uncertainty measurements ($12 \pm 1 $, for instance) as probability distributions that look | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
The first three points, as far as I can tell, are a variation on a single argument.
Scientists often treat uncertainty measurements ($12 \pm 1 $, for instance) as probability distributions that look like this:
When actually, the... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
The first three points, as far as I can tell, are a variation on a single argument.
Scientists often treat uncertainty measurements ($12 \pm 1 $, for instance) as probability distributions that look |
4,007 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | Much of the article and the figure you include make a very simple point:
Lack of evidence for an effect is not evidence that it does not exist.
For example,
"In our study, mice given cyanide did not die at statistically-significantly higher rates" is not evidence for the claim "cyanide has no effect on mouse deaths"... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | Much of the article and the figure you include make a very simple point:
Lack of evidence for an effect is not evidence that it does not exist.
For example,
"In our study, mice given cyanide did no | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
Much of the article and the figure you include make a very simple point:
Lack of evidence for an effect is not evidence that it does not exist.
For example,
"In our study, mice given cyanide did not die at statistically-signifi... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
Much of the article and the figure you include make a very simple point:
Lack of evidence for an effect is not evidence that it does not exist.
For example,
"In our study, mice given cyanide did no |
4,008 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | I'll try.
The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.
Values near the middle of the confidence (compatibility) interval are... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | I'll try.
The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the in | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
I'll try.
The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompat... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
I'll try.
The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the in |
4,009 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | The great XKCD did this cartoon a while ago, illustrating the problem. If results with $P\gt0.05$ are simplistically treated as proving a hypothesis - and all too often they are - then 1 in 20 hypotheses so proven will actually be false. Similarly, if $P\lt0.05$ is taken as disproving a hypotheses then 1 in 20 true hy... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | The great XKCD did this cartoon a while ago, illustrating the problem. If results with $P\gt0.05$ are simplistically treated as proving a hypothesis - and all too often they are - then 1 in 20 hypoth | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
The great XKCD did this cartoon a while ago, illustrating the problem. If results with $P\gt0.05$ are simplistically treated as proving a hypothesis - and all too often they are - then 1 in 20 hypotheses so proven will actually b... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
The great XKCD did this cartoon a while ago, illustrating the problem. If results with $P\gt0.05$ are simplistically treated as proving a hypothesis - and all too often they are - then 1 in 20 hypoth |
4,010 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | tl;dr- It's fundamentally impossible to prove that things are unrelated; statistics can only be used to show when things are related. Despite this well-established fact, people frequently misinterpret a lack of statistical significance to imply a lack of relationship.
A good encryption method should generate a ciphe... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | tl;dr- It's fundamentally impossible to prove that things are unrelated; statistics can only be used to show when things are related. Despite this well-established fact, people frequently misinterpr | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
tl;dr- It's fundamentally impossible to prove that things are unrelated; statistics can only be used to show when things are related. Despite this well-established fact, people frequently misinterpret a lack of statistical signi... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
tl;dr- It's fundamentally impossible to prove that things are unrelated; statistics can only be used to show when things are related. Despite this well-established fact, people frequently misinterpr |
4,011 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | For a didactic introduction to the problem, Alex Reinhart wrote a book fully available online and edited at No Starch Press (with more content):
https://www.statisticsdonewrong.com
It explains the root of the problem without sophisticated maths and has specific chapters with examples from simulated data set:
https://ww... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | For a didactic introduction to the problem, Alex Reinhart wrote a book fully available online and edited at No Starch Press (with more content):
https://www.statisticsdonewrong.com
It explains the roo | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
For a didactic introduction to the problem, Alex Reinhart wrote a book fully available online and edited at No Starch Press (with more content):
https://www.statisticsdonewrong.com
It explains the root of the problem without sophi... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
For a didactic introduction to the problem, Alex Reinhart wrote a book fully available online and edited at No Starch Press (with more content):
https://www.statisticsdonewrong.com
It explains the roo |
4,012 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | For me, the most important part was:
...[We] urge authors to
discuss the point estimate, even when they have a large P value or a
wide interval, as well as discussing the limits of that interval.
In other words: Place a higher emphasis on discussing estimates (center and confidence interval), and
a lower emphasis... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | For me, the most important part was:
...[We] urge authors to
discuss the point estimate, even when they have a large P value or a
wide interval, as well as discussing the limits of that interval. | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
For me, the most important part was:
...[We] urge authors to
discuss the point estimate, even when they have a large P value or a
wide interval, as well as discussing the limits of that interval.
In other words: Place a high... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
For me, the most important part was:
...[We] urge authors to
discuss the point estimate, even when they have a large P value or a
wide interval, as well as discussing the limits of that interval. |
4,013 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | It is "significant" that statisticians, not just scientists, are rising up and objecting to the loose use of "significance" and $P$ values. The most recent issue of The American Statistician is devoted entirely to this matter. See especially the lead editorial by Wasserman, Schirm, and Lazar. | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | It is "significant" that statisticians, not just scientists, are rising up and objecting to the loose use of "significance" and $P$ values. The most recent issue of The American Statistician is devote | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
It is "significant" that statisticians, not just scientists, are rising up and objecting to the loose use of "significance" and $P$ values. The most recent issue of The American Statistician is devoted entirely to this matter. See... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
It is "significant" that statisticians, not just scientists, are rising up and objecting to the loose use of "significance" and $P$ values. The most recent issue of The American Statistician is devote |
4,014 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | It is a fact that for several reasons, p-values have indeed become a problem.
However, despite their weaknesses, they have important advantages such as simplicity and intuitive theory. Therefore, while overall I agree with the Comment in Nature, I do think that rather than ditching statistical significance completely,... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | It is a fact that for several reasons, p-values have indeed become a problem.
However, despite their weaknesses, they have important advantages such as simplicity and intuitive theory. Therefore, whi | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
It is a fact that for several reasons, p-values have indeed become a problem.
However, despite their weaknesses, they have important advantages such as simplicity and intuitive theory. Therefore, while overall I agree with the Co... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
It is a fact that for several reasons, p-values have indeed become a problem.
However, despite their weaknesses, they have important advantages such as simplicity and intuitive theory. Therefore, whi |
4,015 | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | One thing that has not been mentioned is that error or significance are statistical estimates, not actual physical measurements: They depend heavily on the data you have available and how you process it. You can only provide precise value of error and significance if you have measured every possible event. This is usua... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature) | One thing that has not been mentioned is that error or significance are statistical estimates, not actual physical measurements: They depend heavily on the data you have available and how you process | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
One thing that has not been mentioned is that error or significance are statistical estimates, not actual physical measurements: They depend heavily on the data you have available and how you process it. You can only provide preci... | What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
One thing that has not been mentioned is that error or significance are statistical estimates, not actual physical measurements: They depend heavily on the data you have available and how you process |
4,016 | Mean absolute deviation vs. standard deviation | Both answer how far your values are spread around the mean of the observations.
An observation that is 1 under the mean is equally "far" from the mean as a value that is 1 above the mean. Hence you should neglect the sign of the deviation. This can be done in two ways:
Calculate the absolute value of the deviations an... | Mean absolute deviation vs. standard deviation | Both answer how far your values are spread around the mean of the observations.
An observation that is 1 under the mean is equally "far" from the mean as a value that is 1 above the mean. Hence you sh | Mean absolute deviation vs. standard deviation
Both answer how far your values are spread around the mean of the observations.
An observation that is 1 under the mean is equally "far" from the mean as a value that is 1 above the mean. Hence you should neglect the sign of the deviation. This can be done in two ways:
Ca... | Mean absolute deviation vs. standard deviation
Both answer how far your values are spread around the mean of the observations.
An observation that is 1 under the mean is equally "far" from the mean as a value that is 1 above the mean. Hence you sh |
4,017 | Mean absolute deviation vs. standard deviation | Today, statistical values are predominantly calculated by computer programs (Excel, ...), not by hand-held calculators anymore . Hence, I would posit that calculating "mean deviation" is no more cumbersome than calculating "standard deviation". Although standard deviation may have "... mathematical properties that ma... | Mean absolute deviation vs. standard deviation | Today, statistical values are predominantly calculated by computer programs (Excel, ...), not by hand-held calculators anymore . Hence, I would posit that calculating "mean deviation" is no more cumb | Mean absolute deviation vs. standard deviation
Today, statistical values are predominantly calculated by computer programs (Excel, ...), not by hand-held calculators anymore . Hence, I would posit that calculating "mean deviation" is no more cumbersome than calculating "standard deviation". Although standard deviatio... | Mean absolute deviation vs. standard deviation
Today, statistical values are predominantly calculated by computer programs (Excel, ...), not by hand-held calculators anymore . Hence, I would posit that calculating "mean deviation" is no more cumb |
4,018 | Mean absolute deviation vs. standard deviation | They both measure the same concept, but are not equal.
You are comparing $\frac{1}{n} \sum |x_i-\bar{x}|$ with $\sqrt{\frac{1}{n} \sum (x_i-\bar{x})^2}$. They aren't equal for two reasons:
Firstly the square-root operator is not linear, or $\sqrt{a+b} \neq \sqrt{a} + \sqrt{b}$. Therefore the sum of absolute deviations... | Mean absolute deviation vs. standard deviation | They both measure the same concept, but are not equal.
You are comparing $\frac{1}{n} \sum |x_i-\bar{x}|$ with $\sqrt{\frac{1}{n} \sum (x_i-\bar{x})^2}$. They aren't equal for two reasons:
Firstly th | Mean absolute deviation vs. standard deviation
They both measure the same concept, but are not equal.
You are comparing $\frac{1}{n} \sum |x_i-\bar{x}|$ with $\sqrt{\frac{1}{n} \sum (x_i-\bar{x})^2}$. They aren't equal for two reasons:
Firstly the square-root operator is not linear, or $\sqrt{a+b} \neq \sqrt{a} + \sqr... | Mean absolute deviation vs. standard deviation
They both measure the same concept, but are not equal.
You are comparing $\frac{1}{n} \sum |x_i-\bar{x}|$ with $\sqrt{\frac{1}{n} \sum (x_i-\bar{x})^2}$. They aren't equal for two reasons:
Firstly th |
4,019 | Mean absolute deviation vs. standard deviation | Both measure the dispersion of your data by computing the distance of the data to its mean.
the mean absolute deviation is using norm L1 (it is also called Manhattan distance or rectilinear distance)
the standard deviation is using norm L2 (also called Euclidean distance)
The difference between the two norms is that... | Mean absolute deviation vs. standard deviation | Both measure the dispersion of your data by computing the distance of the data to its mean.
the mean absolute deviation is using norm L1 (it is also called Manhattan distance or rectilinear distance) | Mean absolute deviation vs. standard deviation
Both measure the dispersion of your data by computing the distance of the data to its mean.
the mean absolute deviation is using norm L1 (it is also called Manhattan distance or rectilinear distance)
the standard deviation is using norm L2 (also called Euclidean distance... | Mean absolute deviation vs. standard deviation
Both measure the dispersion of your data by computing the distance of the data to its mean.
the mean absolute deviation is using norm L1 (it is also called Manhattan distance or rectilinear distance) |
4,020 | Mean absolute deviation vs. standard deviation | @itsols, I'll add to Kasper's important notion that The mean deviation is rarely used. Why is standard deviation considered generally a better measure of variability than mean absolute deviation? Because arithmetic mean is the locus of minimal sum of squared (and not sum of absolute) deviations from it.
Suppose you wan... | Mean absolute deviation vs. standard deviation | @itsols, I'll add to Kasper's important notion that The mean deviation is rarely used. Why is standard deviation considered generally a better measure of variability than mean absolute deviation? Beca | Mean absolute deviation vs. standard deviation
@itsols, I'll add to Kasper's important notion that The mean deviation is rarely used. Why is standard deviation considered generally a better measure of variability than mean absolute deviation? Because arithmetic mean is the locus of minimal sum of squared (and not sum o... | Mean absolute deviation vs. standard deviation
@itsols, I'll add to Kasper's important notion that The mean deviation is rarely used. Why is standard deviation considered generally a better measure of variability than mean absolute deviation? Beca |
4,021 | Mean absolute deviation vs. standard deviation | One thing worth adding is that the most likely reason your 30-year-old textbook used the absolute mean deviation as opposed to standard deviation is that it is easier to calculate by hand (no squaring / square roots). Now that calculators are readily accessible to high school students, there is no reason not to ask th... | Mean absolute deviation vs. standard deviation | One thing worth adding is that the most likely reason your 30-year-old textbook used the absolute mean deviation as opposed to standard deviation is that it is easier to calculate by hand (no squaring | Mean absolute deviation vs. standard deviation
One thing worth adding is that the most likely reason your 30-year-old textbook used the absolute mean deviation as opposed to standard deviation is that it is easier to calculate by hand (no squaring / square roots). Now that calculators are readily accessible to high sc... | Mean absolute deviation vs. standard deviation
One thing worth adding is that the most likely reason your 30-year-old textbook used the absolute mean deviation as opposed to standard deviation is that it is easier to calculate by hand (no squaring |
4,022 | Mean absolute deviation vs. standard deviation | They are similar measures that try to quantify the same notion. Typically you use st. deviation since it has nice properties, if you make some assumption about the underlying distribution.
On the other hand the absolute value in mean deviation causes some issues from a mathematical perspective since you can't differen... | Mean absolute deviation vs. standard deviation | They are similar measures that try to quantify the same notion. Typically you use st. deviation since it has nice properties, if you make some assumption about the underlying distribution.
On the oth | Mean absolute deviation vs. standard deviation
They are similar measures that try to quantify the same notion. Typically you use st. deviation since it has nice properties, if you make some assumption about the underlying distribution.
On the other hand the absolute value in mean deviation causes some issues from a ma... | Mean absolute deviation vs. standard deviation
They are similar measures that try to quantify the same notion. Typically you use st. deviation since it has nice properties, if you make some assumption about the underlying distribution.
On the oth |
4,023 | Mean absolute deviation vs. standard deviation | No. You are wrong. Just kidding. There are, however, many viable reasons why one would want to compute mean deviation rather than formal std, and in this way I am in agreement with the viewpoint of my engineering Brethren. Certainly if I am computing statistics to compare with a body of existing work which is expre... | Mean absolute deviation vs. standard deviation | No. You are wrong. Just kidding. There are, however, many viable reasons why one would want to compute mean deviation rather than formal std, and in this way I am in agreement with the viewpoint of | Mean absolute deviation vs. standard deviation
No. You are wrong. Just kidding. There are, however, many viable reasons why one would want to compute mean deviation rather than formal std, and in this way I am in agreement with the viewpoint of my engineering Brethren. Certainly if I am computing statistics to comp... | Mean absolute deviation vs. standard deviation
No. You are wrong. Just kidding. There are, however, many viable reasons why one would want to compute mean deviation rather than formal std, and in this way I am in agreement with the viewpoint of |
4,024 | Mean absolute deviation vs. standard deviation | Amar Sagoo has a very good article explaining this.
To add my own attempt at an intuitive understanding:
Mean deviation is a decent way of asking how far a hypothetical "average" point is from the mean, but it doesn't really work for asking how far all the points are from each other, or how "spread out" the data are.
S... | Mean absolute deviation vs. standard deviation | Amar Sagoo has a very good article explaining this.
To add my own attempt at an intuitive understanding:
Mean deviation is a decent way of asking how far a hypothetical "average" point is from the mea | Mean absolute deviation vs. standard deviation
Amar Sagoo has a very good article explaining this.
To add my own attempt at an intuitive understanding:
Mean deviation is a decent way of asking how far a hypothetical "average" point is from the mean, but it doesn't really work for asking how far all the points are from ... | Mean absolute deviation vs. standard deviation
Amar Sagoo has a very good article explaining this.
To add my own attempt at an intuitive understanding:
Mean deviation is a decent way of asking how far a hypothetical "average" point is from the mea |
4,025 | Mean absolute deviation vs. standard deviation | The standard deviation represents dispersion due to random processes. Specifically, many physical measurements which are expected to be due to the sum of many independent processes have normal (bell curve) distributions.
The normal probability distribution is given by:
$
\Large Y = \frac{1}{\sigma\sqrt{2\pi}}e^{-\frac... | Mean absolute deviation vs. standard deviation | The standard deviation represents dispersion due to random processes. Specifically, many physical measurements which are expected to be due to the sum of many independent processes have normal (bell c | Mean absolute deviation vs. standard deviation
The standard deviation represents dispersion due to random processes. Specifically, many physical measurements which are expected to be due to the sum of many independent processes have normal (bell curve) distributions.
The normal probability distribution is given by:
$
... | Mean absolute deviation vs. standard deviation
The standard deviation represents dispersion due to random processes. Specifically, many physical measurements which are expected to be due to the sum of many independent processes have normal (bell c |
4,026 | Mean absolute deviation vs. standard deviation | Consider three set of data having same mean and MD but their ranges are changing. It is interesting to see how SD changes with change in the range of the data.
SET 1: 1, 3,5,7,9,11,13,15,17,19 Range:1-19 Mean=10, MD=5 SD= 6.05
SET 2: 2,3,5,7,7,9,13,15,14,23 Range: 1-23 Mean=10 MD=5 SD=6.28
SET 3: 3,5,5,7,7,8,10,12,13... | Mean absolute deviation vs. standard deviation | Consider three set of data having same mean and MD but their ranges are changing. It is interesting to see how SD changes with change in the range of the data.
SET 1: 1, 3,5,7,9,11,13,15,17,19 Range: | Mean absolute deviation vs. standard deviation
Consider three set of data having same mean and MD but their ranges are changing. It is interesting to see how SD changes with change in the range of the data.
SET 1: 1, 3,5,7,9,11,13,15,17,19 Range:1-19 Mean=10, MD=5 SD= 6.05
SET 2: 2,3,5,7,7,9,13,15,14,23 Range: 1-23 M... | Mean absolute deviation vs. standard deviation
Consider three set of data having same mean and MD but their ranges are changing. It is interesting to see how SD changes with change in the range of the data.
SET 1: 1, 3,5,7,9,11,13,15,17,19 Range: |
4,027 | Mean absolute deviation vs. standard deviation | The two measures differ indeed. The first is often referred to as Mean Absolute Deviation (MAD) and the second is Standard Deviation (STD). In embedded applications with severely limited computing power and limited program memory, avoiding the square root calculations can be very desirable.
From a quick rough test it ... | Mean absolute deviation vs. standard deviation | The two measures differ indeed. The first is often referred to as Mean Absolute Deviation (MAD) and the second is Standard Deviation (STD). In embedded applications with severely limited computing pow | Mean absolute deviation vs. standard deviation
The two measures differ indeed. The first is often referred to as Mean Absolute Deviation (MAD) and the second is Standard Deviation (STD). In embedded applications with severely limited computing power and limited program memory, avoiding the square root calculations can ... | Mean absolute deviation vs. standard deviation
The two measures differ indeed. The first is often referred to as Mean Absolute Deviation (MAD) and the second is Standard Deviation (STD). In embedded applications with severely limited computing pow |
4,028 | Mean absolute deviation vs. standard deviation | Each of the three parameters - Mean (M), Mean Absolute Deviation (MAD) and Standard Deviation (σ), calculated for a set, provide some unique information about the set which the other two parameters don't. σ loosely includes the information provided by MAD, but it isn't vice versa. Hence, σ is conveniently used everywhe... | Mean absolute deviation vs. standard deviation | Each of the three parameters - Mean (M), Mean Absolute Deviation (MAD) and Standard Deviation (σ), calculated for a set, provide some unique information about the set which the other two parameters do | Mean absolute deviation vs. standard deviation
Each of the three parameters - Mean (M), Mean Absolute Deviation (MAD) and Standard Deviation (σ), calculated for a set, provide some unique information about the set which the other two parameters don't. σ loosely includes the information provided by MAD, but it isn't vic... | Mean absolute deviation vs. standard deviation
Each of the three parameters - Mean (M), Mean Absolute Deviation (MAD) and Standard Deviation (σ), calculated for a set, provide some unique information about the set which the other two parameters do |
4,029 | Intuitive explanation of the bias-variance tradeoff? | Imagine some 2D data--let's say height versus weight for students at a high school--plotted on a pair of axes.
Now suppose you fit a straight line through it. This line, which of course represents a set of predicted values, has zero statistical variance. But the bias is (probably) high--i.e., it doesn't fit the data ve... | Intuitive explanation of the bias-variance tradeoff? | Imagine some 2D data--let's say height versus weight for students at a high school--plotted on a pair of axes.
Now suppose you fit a straight line through it. This line, which of course represents a s | Intuitive explanation of the bias-variance tradeoff?
Imagine some 2D data--let's say height versus weight for students at a high school--plotted on a pair of axes.
Now suppose you fit a straight line through it. This line, which of course represents a set of predicted values, has zero statistical variance. But the bias... | Intuitive explanation of the bias-variance tradeoff?
Imagine some 2D data--let's say height versus weight for students at a high school--plotted on a pair of axes.
Now suppose you fit a straight line through it. This line, which of course represents a s |
4,030 | Intuitive explanation of the bias-variance tradeoff? | Let's say you are considering catastrophic health insurance, and there is a 1% probability of getting sick which would cost 1 million dollars. The expected cost of getting sick is thus 10,000 dollars. The insurance company, wanting to make a profit, will charge you 15,000 for the policy.
Buying the policy gives an e... | Intuitive explanation of the bias-variance tradeoff? | Let's say you are considering catastrophic health insurance, and there is a 1% probability of getting sick which would cost 1 million dollars. The expected cost of getting sick is thus 10,000 dollars | Intuitive explanation of the bias-variance tradeoff?
Let's say you are considering catastrophic health insurance, and there is a 1% probability of getting sick which would cost 1 million dollars. The expected cost of getting sick is thus 10,000 dollars. The insurance company, wanting to make a profit, will charge you ... | Intuitive explanation of the bias-variance tradeoff?
Let's say you are considering catastrophic health insurance, and there is a 1% probability of getting sick which would cost 1 million dollars. The expected cost of getting sick is thus 10,000 dollars |
4,031 | Intuitive explanation of the bias-variance tradeoff? | First, lets understand the meaning of bias and variance:
Imagine the center of the red bulls' eye region is the true mean value of our target random variable which we are trying to predict. Every time we take a sample set of observations and predict the value of this variable we plot a blue dot. We predicted correctly... | Intuitive explanation of the bias-variance tradeoff? | First, lets understand the meaning of bias and variance:
Imagine the center of the red bulls' eye region is the true mean value of our target random variable which we are trying to predict. Every tim | Intuitive explanation of the bias-variance tradeoff?
First, lets understand the meaning of bias and variance:
Imagine the center of the red bulls' eye region is the true mean value of our target random variable which we are trying to predict. Every time we take a sample set of observations and predict the value of thi... | Intuitive explanation of the bias-variance tradeoff?
First, lets understand the meaning of bias and variance:
Imagine the center of the red bulls' eye region is the true mean value of our target random variable which we are trying to predict. Every tim |
4,032 | Intuitive explanation of the bias-variance tradeoff? | I highly recommend having a look at Caltech ML course by Yaser Abu-Mostafa, Lecture 8 (Bias-Variance Tradeoff) . Here are the outlines:
Say you are trying to learn the sine function:
Our training set consists of only 2 data points.
Let's try to do it with two models, $h_0(x)=b$ and $h_1(x)=ax+b$:
For $h_0(x)=b$, when ... | Intuitive explanation of the bias-variance tradeoff? | I highly recommend having a look at Caltech ML course by Yaser Abu-Mostafa, Lecture 8 (Bias-Variance Tradeoff) . Here are the outlines:
Say you are trying to learn the sine function:
Our training set | Intuitive explanation of the bias-variance tradeoff?
I highly recommend having a look at Caltech ML course by Yaser Abu-Mostafa, Lecture 8 (Bias-Variance Tradeoff) . Here are the outlines:
Say you are trying to learn the sine function:
Our training set consists of only 2 data points.
Let's try to do it with two models... | Intuitive explanation of the bias-variance tradeoff?
I highly recommend having a look at Caltech ML course by Yaser Abu-Mostafa, Lecture 8 (Bias-Variance Tradeoff) . Here are the outlines:
Say you are trying to learn the sine function:
Our training set |
4,033 | Intuitive explanation of the bias-variance tradeoff? | The basic idea is that too simple a model will underfit (high bias)
while too complex a model will overfit (high variance) and that bias
and variance trade off as model complexity is varied.
(Neal, 2019)
However, while bias-variance tradeoff seems to hold for some simple algorithms like linear regression, or $k$-NN, i... | Intuitive explanation of the bias-variance tradeoff? | The basic idea is that too simple a model will underfit (high bias)
while too complex a model will overfit (high variance) and that bias
and variance trade off as model complexity is varied.
(Neal, 2 | Intuitive explanation of the bias-variance tradeoff?
The basic idea is that too simple a model will underfit (high bias)
while too complex a model will overfit (high variance) and that bias
and variance trade off as model complexity is varied.
(Neal, 2019)
However, while bias-variance tradeoff seems to hold for some s... | Intuitive explanation of the bias-variance tradeoff?
The basic idea is that too simple a model will underfit (high bias)
while too complex a model will overfit (high variance) and that bias
and variance trade off as model complexity is varied.
(Neal, 2 |
4,034 | Intuitive explanation of the bias-variance tradeoff? | Here is a very simple explanation. Imagine you have a scatter plot of points {x_i,y_i} that were sampled from some distribution. You want to fit some model to it. You can choose a linear curve or a higher order polynomial curve or something else. Whatever you choose is going to be applied to predict new y values for a ... | Intuitive explanation of the bias-variance tradeoff? | Here is a very simple explanation. Imagine you have a scatter plot of points {x_i,y_i} that were sampled from some distribution. You want to fit some model to it. You can choose a linear curve or a hi | Intuitive explanation of the bias-variance tradeoff?
Here is a very simple explanation. Imagine you have a scatter plot of points {x_i,y_i} that were sampled from some distribution. You want to fit some model to it. You can choose a linear curve or a higher order polynomial curve or something else. Whatever you choose ... | Intuitive explanation of the bias-variance tradeoff?
Here is a very simple explanation. Imagine you have a scatter plot of points {x_i,y_i} that were sampled from some distribution. You want to fit some model to it. You can choose a linear curve or a hi |
4,035 | Intuitive explanation of the bias-variance tradeoff? | Imagine if model building task could be repeated for different training datasets, i.e. we train a new model for different dataset every time(shown in the figure below). If we fix a test data point and evaluate model prediction on this point, the predictions will be varied due to randomness in the model generation proce... | Intuitive explanation of the bias-variance tradeoff? | Imagine if model building task could be repeated for different training datasets, i.e. we train a new model for different dataset every time(shown in the figure below). If we fix a test data point and | Intuitive explanation of the bias-variance tradeoff?
Imagine if model building task could be repeated for different training datasets, i.e. we train a new model for different dataset every time(shown in the figure below). If we fix a test data point and evaluate model prediction on this point, the predictions will be v... | Intuitive explanation of the bias-variance tradeoff?
Imagine if model building task could be repeated for different training datasets, i.e. we train a new model for different dataset every time(shown in the figure below). If we fix a test data point and |
4,036 | What is the difference between prediction and inference? | Inference: Given a set of data you want to infer how the output is generated as a function of the data.
Prediction: Given a new measurement, you want to use an existing data set to build a model that reliably chooses the correct identifier from a set of outcomes.
Inference: You want to find out what the effect of Age... | What is the difference between prediction and inference? | Inference: Given a set of data you want to infer how the output is generated as a function of the data.
Prediction: Given a new measurement, you want to use an existing data set to build a model that | What is the difference between prediction and inference?
Inference: Given a set of data you want to infer how the output is generated as a function of the data.
Prediction: Given a new measurement, you want to use an existing data set to build a model that reliably chooses the correct identifier from a set of outcomes... | What is the difference between prediction and inference?
Inference: Given a set of data you want to infer how the output is generated as a function of the data.
Prediction: Given a new measurement, you want to use an existing data set to build a model that |
4,037 | What is the difference between prediction and inference? | In page 20 of the book, the authors provide a beautiful example which made me understand the difference.
Here's the paragraph from the book : An Introduction to Statistical Learning
"
For example, in a real estate setting, one may seek to relate values of
homes to inputs such as crime rate, zoning, distance from a rive... | What is the difference between prediction and inference? | In page 20 of the book, the authors provide a beautiful example which made me understand the difference.
Here's the paragraph from the book : An Introduction to Statistical Learning
"
For example, in | What is the difference between prediction and inference?
In page 20 of the book, the authors provide a beautiful example which made me understand the difference.
Here's the paragraph from the book : An Introduction to Statistical Learning
"
For example, in a real estate setting, one may seek to relate values of
homes t... | What is the difference between prediction and inference?
In page 20 of the book, the authors provide a beautiful example which made me understand the difference.
Here's the paragraph from the book : An Introduction to Statistical Learning
"
For example, in |
4,038 | What is the difference between prediction and inference? | Generally when doing data analysis we imagine that there is some kind of "data generating process" which gives rise to the data, and inference refers to learning about the structure of this process while prediction means being able to actually forecast the data that come from it. Oftentimes the two go together, but no... | What is the difference between prediction and inference? | Generally when doing data analysis we imagine that there is some kind of "data generating process" which gives rise to the data, and inference refers to learning about the structure of this process wh | What is the difference between prediction and inference?
Generally when doing data analysis we imagine that there is some kind of "data generating process" which gives rise to the data, and inference refers to learning about the structure of this process while prediction means being able to actually forecast the data t... | What is the difference between prediction and inference?
Generally when doing data analysis we imagine that there is some kind of "data generating process" which gives rise to the data, and inference refers to learning about the structure of this process wh |
4,039 | What is the difference between prediction and inference? | Prediction uses estimated f to forecast into the future. Suppose you observe a variable $y_t$, maybe it's the revenue of the store. You want to make financial plans for your business, and need to forecast the revenue in next quarter. You suspect that the revenue depends on the income of population in this quarter $x_{1... | What is the difference between prediction and inference? | Prediction uses estimated f to forecast into the future. Suppose you observe a variable $y_t$, maybe it's the revenue of the store. You want to make financial plans for your business, and need to fore | What is the difference between prediction and inference?
Prediction uses estimated f to forecast into the future. Suppose you observe a variable $y_t$, maybe it's the revenue of the store. You want to make financial plans for your business, and need to forecast the revenue in next quarter. You suspect that the revenue ... | What is the difference between prediction and inference?
Prediction uses estimated f to forecast into the future. Suppose you observe a variable $y_t$, maybe it's the revenue of the store. You want to make financial plans for your business, and need to fore |
4,040 | What is the difference between prediction and inference? | You are not alone here.
After reading answers, I am not confused anymore - not because I understand the difference, but because I understand it is in the eye of the beholder and verbally induced.
I am sure now those two terms are political definitions rather than scientific ones.
Take for example the explanation from ... | What is the difference between prediction and inference? | You are not alone here.
After reading answers, I am not confused anymore - not because I understand the difference, but because I understand it is in the eye of the beholder and verbally induced.
I a | What is the difference between prediction and inference?
You are not alone here.
After reading answers, I am not confused anymore - not because I understand the difference, but because I understand it is in the eye of the beholder and verbally induced.
I am sure now those two terms are political definitions rather tha... | What is the difference between prediction and inference?
You are not alone here.
After reading answers, I am not confused anymore - not because I understand the difference, but because I understand it is in the eye of the beholder and verbally induced.
I a |
4,041 | What is the difference between prediction and inference? | Imagine, you are a medical doctor on an intensive care unit. You have a patient with a strong fever and a given number of blood cells and a given body weight and a hundred different data and you want to predict, if he or she is going to survive. If yes, he is going to conceal that story about his other kid to his wife,... | What is the difference between prediction and inference? | Imagine, you are a medical doctor on an intensive care unit. You have a patient with a strong fever and a given number of blood cells and a given body weight and a hundred different data and you want | What is the difference between prediction and inference?
Imagine, you are a medical doctor on an intensive care unit. You have a patient with a strong fever and a given number of blood cells and a given body weight and a hundred different data and you want to predict, if he or she is going to survive. If yes, he is goi... | What is the difference between prediction and inference?
Imagine, you are a medical doctor on an intensive care unit. You have a patient with a strong fever and a given number of blood cells and a given body weight and a hundred different data and you want |
4,042 | What is the difference between prediction and inference? | Given a data set of $n=100$ observations, $k=50$ independent variables $x_i$, and one dependent variable $y$, inference answers questions such as:
What subset or combination of the $k$ independent variables affect $y$?
If I were able to increase the value of $x_1$ by 10%, how much would $y$ increase? (i.e. $\frac{\par... | What is the difference between prediction and inference? | Given a data set of $n=100$ observations, $k=50$ independent variables $x_i$, and one dependent variable $y$, inference answers questions such as:
What subset or combination of the $k$ independent va | What is the difference between prediction and inference?
Given a data set of $n=100$ observations, $k=50$ independent variables $x_i$, and one dependent variable $y$, inference answers questions such as:
What subset or combination of the $k$ independent variables affect $y$?
If I were able to increase the value of $x_... | What is the difference between prediction and inference?
Given a data set of $n=100$ observations, $k=50$ independent variables $x_i$, and one dependent variable $y$, inference answers questions such as:
What subset or combination of the $k$ independent va |
4,043 | What is the difference between prediction and inference? | I know many answers have been posted already, but for those of you who don't read the book (Introduction to Statistical Learning), here's three exercises found in the second chapter. See if you can solve them, they helped me quite a bit to understand the difference between inference and prediction.
Explain whether eac... | What is the difference between prediction and inference? | I know many answers have been posted already, but for those of you who don't read the book (Introduction to Statistical Learning), here's three exercises found in the second chapter. See if you can so | What is the difference between prediction and inference?
I know many answers have been posted already, but for those of you who don't read the book (Introduction to Statistical Learning), here's three exercises found in the second chapter. See if you can solve them, they helped me quite a bit to understand the differen... | What is the difference between prediction and inference?
I know many answers have been posted already, but for those of you who don't read the book (Introduction to Statistical Learning), here's three exercises found in the second chapter. See if you can so |
4,044 | What is the difference between prediction and inference? | There's good research showing that a strong predictor of whether borrowers will repay their loans is whether they use felt to protect their floors from being scratched by furniture legs. This "felt" variable will be a distinct aid to a predictive model where the outcome is repay vs. default. However, if lenders want ... | What is the difference between prediction and inference? | There's good research showing that a strong predictor of whether borrowers will repay their loans is whether they use felt to protect their floors from being scratched by furniture legs. This "felt" | What is the difference between prediction and inference?
There's good research showing that a strong predictor of whether borrowers will repay their loans is whether they use felt to protect their floors from being scratched by furniture legs. This "felt" variable will be a distinct aid to a predictive model where the... | What is the difference between prediction and inference?
There's good research showing that a strong predictor of whether borrowers will repay their loans is whether they use felt to protect their floors from being scratched by furniture legs. This "felt" |
4,045 | What is the difference between prediction and inference? | y = f(x) then
prediction(what is the value of Y with a given value of x: if specific value of x what could be the value of Y
inference(how y changes with change in x) : what could be the affect on Y if x changes
Prediction example : suppose y represent the salary of a person then if we provide input such as years of ex... | What is the difference between prediction and inference? | y = f(x) then
prediction(what is the value of Y with a given value of x: if specific value of x what could be the value of Y
inference(how y changes with change in x) : what could be the affect on Y i | What is the difference between prediction and inference?
y = f(x) then
prediction(what is the value of Y with a given value of x: if specific value of x what could be the value of Y
inference(how y changes with change in x) : what could be the affect on Y if x changes
Prediction example : suppose y represent the salary... | What is the difference between prediction and inference?
y = f(x) then
prediction(what is the value of Y with a given value of x: if specific value of x what could be the value of Y
inference(how y changes with change in x) : what could be the affect on Y i |
4,046 | How to generate correlated random numbers (given means, variances and degree of correlation)? | To answer your question on "a good, ideally quick way to generate correlated random numbers":
Given a desired variance-covariance matrix $C$ that is by definition positive definite, the Cholesky decomposition of it is: $C$=$LL^T$; $L$ being lower triangular matrix.
If you now use this matrix $L$ to project an uncorrel... | How to generate correlated random numbers (given means, variances and degree of correlation)? | To answer your question on "a good, ideally quick way to generate correlated random numbers":
Given a desired variance-covariance matrix $C$ that is by definition positive definite, the Cholesky decom | How to generate correlated random numbers (given means, variances and degree of correlation)?
To answer your question on "a good, ideally quick way to generate correlated random numbers":
Given a desired variance-covariance matrix $C$ that is by definition positive definite, the Cholesky decomposition of it is: $C$=$LL... | How to generate correlated random numbers (given means, variances and degree of correlation)?
To answer your question on "a good, ideally quick way to generate correlated random numbers":
Given a desired variance-covariance matrix $C$ that is by definition positive definite, the Cholesky decom |
4,047 | How to generate correlated random numbers (given means, variances and degree of correlation)? | +1 to @user11852, and @jem77bfp, these are good answers. Let me approach this from a different perspective, not because I think it's necessarily better in practice, but because I think it's instructive. Here are a few relevant facts that we know already:
$r$ is the slope of the regression line when both $X$ and $Y... | How to generate correlated random numbers (given means, variances and degree of correlation)? | +1 to @user11852, and @jem77bfp, these are good answers. Let me approach this from a different perspective, not because I think it's necessarily better in practice, but because I think it's instructi | How to generate correlated random numbers (given means, variances and degree of correlation)?
+1 to @user11852, and @jem77bfp, these are good answers. Let me approach this from a different perspective, not because I think it's necessarily better in practice, but because I think it's instructive. Here are a few releva... | How to generate correlated random numbers (given means, variances and degree of correlation)?
+1 to @user11852, and @jem77bfp, these are good answers. Let me approach this from a different perspective, not because I think it's necessarily better in practice, but because I think it's instructi |
4,048 | How to generate correlated random numbers (given means, variances and degree of correlation)? | In general this not a simple thing to do, but I believe there are packages for multivariate normal variable generation (at least in R, see mvrnorm in the MASS package), where you just input a covariance matrix and a mean vector.
There is also one more "constructive" approach. Let's say we want to model a random vector... | How to generate correlated random numbers (given means, variances and degree of correlation)? | In general this not a simple thing to do, but I believe there are packages for multivariate normal variable generation (at least in R, see mvrnorm in the MASS package), where you just input a covarian | How to generate correlated random numbers (given means, variances and degree of correlation)?
In general this not a simple thing to do, but I believe there are packages for multivariate normal variable generation (at least in R, see mvrnorm in the MASS package), where you just input a covariance matrix and a mean vecto... | How to generate correlated random numbers (given means, variances and degree of correlation)?
In general this not a simple thing to do, but I believe there are packages for multivariate normal variable generation (at least in R, see mvrnorm in the MASS package), where you just input a covarian |
4,049 | How to generate correlated random numbers (given means, variances and degree of correlation)? | If you are ready to give up efficiency, you can use a throw-away alogorithm. Its advantage is, that it allows for any kind of distributions (not only Gaussian).
Start by generating two uncorrelated sequences of random numbers $\{x_i\}_{i=1}^N$ and $\{y_i\}_{i=1}^N$ with any desired distributions. Let $C$ by the desired... | How to generate correlated random numbers (given means, variances and degree of correlation)? | If you are ready to give up efficiency, you can use a throw-away alogorithm. Its advantage is, that it allows for any kind of distributions (not only Gaussian).
Start by generating two uncorrelated se | How to generate correlated random numbers (given means, variances and degree of correlation)?
If you are ready to give up efficiency, you can use a throw-away alogorithm. Its advantage is, that it allows for any kind of distributions (not only Gaussian).
Start by generating two uncorrelated sequences of random numbers ... | How to generate correlated random numbers (given means, variances and degree of correlation)?
If you are ready to give up efficiency, you can use a throw-away alogorithm. Its advantage is, that it allows for any kind of distributions (not only Gaussian).
Start by generating two uncorrelated se |
4,050 | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | It seems your question more generally addresses the problem of identifying good predictors. In this case, you should consider using some kind of penalized regression (methods dealing with variable or feature selection are relevant too), with e.g. L1, L2 (or a combination thereof, the so-called elasticnet) penalties (lo... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | It seems your question more generally addresses the problem of identifying good predictors. In this case, you should consider using some kind of penalized regression (methods dealing with variable or | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
It seems your question more generally addresses the problem of identifying good predictors. In this case, you should consider using some kind of penalized regression (methods dealing with variable or feature selection are relevant too)... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
It seems your question more generally addresses the problem of identifying good predictors. In this case, you should consider using some kind of penalized regression (methods dealing with variable or |
4,051 | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | To a great degree you can do whatever you like provided you hold out enough data at random to test whatever model you come up with based on the retained data. A 50% split can be a good idea. Yes, you lose some ability to detect relationships, but what you gain is enormous; namely, the ability to replicate your work b... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | To a great degree you can do whatever you like provided you hold out enough data at random to test whatever model you come up with based on the retained data. A 50% split can be a good idea. Yes, yo | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
To a great degree you can do whatever you like provided you hold out enough data at random to test whatever model you come up with based on the retained data. A 50% split can be a good idea. Yes, you lose some ability to detect relat... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
To a great degree you can do whatever you like provided you hold out enough data at random to test whatever model you come up with based on the retained data. A 50% split can be a good idea. Yes, yo |
4,052 | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | I think this is a very good question; it gets to the heart of the contentious multiple testing "problem" that plagues fields ranging from epidemiology to econometrics. After all, how can we know if the significance we find is spurious or not? How true is our multivariable model?
In terms of technical approaches to offs... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | I think this is a very good question; it gets to the heart of the contentious multiple testing "problem" that plagues fields ranging from epidemiology to econometrics. After all, how can we know if th | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
I think this is a very good question; it gets to the heart of the contentious multiple testing "problem" that plagues fields ranging from epidemiology to econometrics. After all, how can we know if the significance we find is spurious ... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
I think this is a very good question; it gets to the heart of the contentious multiple testing "problem" that plagues fields ranging from epidemiology to econometrics. After all, how can we know if th |
4,053 | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | There are good answers here. Let me add a couple of small points that I don't see covered elsewhere.
First, what is the nature of your response variables? More specifically, are they understood as related to each other? You should only do two separate multiple regressions if they are understood to be independent (... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | There are good answers here. Let me add a couple of small points that I don't see covered elsewhere.
First, what is the nature of your response variables? More specifically, are they understood as | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
There are good answers here. Let me add a couple of small points that I don't see covered elsewhere.
First, what is the nature of your response variables? More specifically, are they understood as related to each other? You should... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
There are good answers here. Let me add a couple of small points that I don't see covered elsewhere.
First, what is the nature of your response variables? More specifically, are they understood as |
4,054 | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | You can do a seemingly unrelated regression and use an F test. Put your data in a form like this:
Out1 1 P11 P12 0 0 0
Out2 0 0 0 1 P21 P22
so that the predictors for your first outcome have their values when that outcome is the y variable and 0 otherwise and vice-versa. So your y is a list of both outcomes. P... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea? | You can do a seemingly unrelated regression and use an F test. Put your data in a form like this:
Out1 1 P11 P12 0 0 0
Out2 0 0 0 1 P21 P22
so that the predictors for your first outcome have | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
You can do a seemingly unrelated regression and use an F test. Put your data in a form like this:
Out1 1 P11 P12 0 0 0
Out2 0 0 0 1 P21 P22
so that the predictors for your first outcome have their values when that outcome is t... | Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
You can do a seemingly unrelated regression and use an F test. Put your data in a form like this:
Out1 1 P11 P12 0 0 0
Out2 0 0 0 1 P21 P22
so that the predictors for your first outcome have |
4,055 | How can a distribution have infinite mean and variance? | The mean and variance are defined in terms of (sufficiently general) integrals. What it means for the mean or variance to be infinite is a statement about the limiting behavior for those integrals
For example, for a continuous density the mean is $\lim_{a,b\to\infty}\int_{-a}^b x f(x)\ dx$ (which might here be conside... | How can a distribution have infinite mean and variance? | The mean and variance are defined in terms of (sufficiently general) integrals. What it means for the mean or variance to be infinite is a statement about the limiting behavior for those integrals
For | How can a distribution have infinite mean and variance?
The mean and variance are defined in terms of (sufficiently general) integrals. What it means for the mean or variance to be infinite is a statement about the limiting behavior for those integrals
For example, for a continuous density the mean is $\lim_{a,b\to\inf... | How can a distribution have infinite mean and variance?
The mean and variance are defined in terms of (sufficiently general) integrals. What it means for the mean or variance to be infinite is a statement about the limiting behavior for those integrals
For |
4,056 | How can a distribution have infinite mean and variance? | It's instructive to see what goes wrong -- the integrals are all very well, but a sample average is always finite, so what is the issue?
I'll use the Cauchy distribution, which has no finite mean. The distribution is symmetric around zero, so if it had a mean, zero would be that mean. Here are cumulative averages of t... | How can a distribution have infinite mean and variance? | It's instructive to see what goes wrong -- the integrals are all very well, but a sample average is always finite, so what is the issue?
I'll use the Cauchy distribution, which has no finite mean. The | How can a distribution have infinite mean and variance?
It's instructive to see what goes wrong -- the integrals are all very well, but a sample average is always finite, so what is the issue?
I'll use the Cauchy distribution, which has no finite mean. The distribution is symmetric around zero, so if it had a mean, zer... | How can a distribution have infinite mean and variance?
It's instructive to see what goes wrong -- the integrals are all very well, but a sample average is always finite, so what is the issue?
I'll use the Cauchy distribution, which has no finite mean. The |
4,057 | How can a distribution have infinite mean and variance? | Stable distributions provide nice, parametric examples of what you're looking for:
infinite mean and variance: $0 < \text{stability parameter} < 1$
N/A
finite mean and infinite variance: $1 \leq \text{stability parameter} < 2$
finite mean and variance: $\text{stability parameter} = 2$ (Gaussian) | How can a distribution have infinite mean and variance? | Stable distributions provide nice, parametric examples of what you're looking for:
infinite mean and variance: $0 < \text{stability parameter} < 1$
N/A
finite mean and infinite variance: $1 \leq \tex | How can a distribution have infinite mean and variance?
Stable distributions provide nice, parametric examples of what you're looking for:
infinite mean and variance: $0 < \text{stability parameter} < 1$
N/A
finite mean and infinite variance: $1 \leq \text{stability parameter} < 2$
finite mean and variance: $\text{sta... | How can a distribution have infinite mean and variance?
Stable distributions provide nice, parametric examples of what you're looking for:
infinite mean and variance: $0 < \text{stability parameter} < 1$
N/A
finite mean and infinite variance: $1 \leq \tex |
4,058 | How can a distribution have infinite mean and variance? | No one has mentioned the St. Petersburg paradox here; otherwise I wouldn't post in a thread this old that already has multiple answers including one "accepted" answer.
If a coin lands "heads" you win one cent.
If "tails", the winnings double and then if "heads" on the second toss, you win two cents.
If "tails" the seco... | How can a distribution have infinite mean and variance? | No one has mentioned the St. Petersburg paradox here; otherwise I wouldn't post in a thread this old that already has multiple answers including one "accepted" answer.
If a coin lands "heads" you win | How can a distribution have infinite mean and variance?
No one has mentioned the St. Petersburg paradox here; otherwise I wouldn't post in a thread this old that already has multiple answers including one "accepted" answer.
If a coin lands "heads" you win one cent.
If "tails", the winnings double and then if "heads" on... | How can a distribution have infinite mean and variance?
No one has mentioned the St. Petersburg paradox here; otherwise I wouldn't post in a thread this old that already has multiple answers including one "accepted" answer.
If a coin lands "heads" you win |
4,059 | How can a distribution have infinite mean and variance? | For simplicity, suppose we are dealing with an absolutely continuous distribution with density function $f_X$ with some corresponding non-negative kernel function $g_X \propto f_X$. Suppose we consider the general $k$th absolute moment, which is given by the following integral expressions:
$$\mathbb{E}(|X^k|)
= \int ... | How can a distribution have infinite mean and variance? | For simplicity, suppose we are dealing with an absolutely continuous distribution with density function $f_X$ with some corresponding non-negative kernel function $g_X \propto f_X$. Suppose we consid | How can a distribution have infinite mean and variance?
For simplicity, suppose we are dealing with an absolutely continuous distribution with density function $f_X$ with some corresponding non-negative kernel function $g_X \propto f_X$. Suppose we consider the general $k$th absolute moment, which is given by the foll... | How can a distribution have infinite mean and variance?
For simplicity, suppose we are dealing with an absolutely continuous distribution with density function $f_X$ with some corresponding non-negative kernel function $g_X \propto f_X$. Suppose we consid |
4,060 | How can a distribution have infinite mean and variance? | About the second distribution you are looking for, consider the random variable $$ X_2 = \text{number of times you can zoom in like 10cm into a fractal} $$
then the answer is infinite with probability one, and therefore the variance is zero and the mean of the distribution has a value of infinite. | How can a distribution have infinite mean and variance? | About the second distribution you are looking for, consider the random variable $$ X_2 = \text{number of times you can zoom in like 10cm into a fractal} $$
then the answer is infinite with probability | How can a distribution have infinite mean and variance?
About the second distribution you are looking for, consider the random variable $$ X_2 = \text{number of times you can zoom in like 10cm into a fractal} $$
then the answer is infinite with probability one, and therefore the variance is zero and the mean of the dis... | How can a distribution have infinite mean and variance?
About the second distribution you are looking for, consider the random variable $$ X_2 = \text{number of times you can zoom in like 10cm into a fractal} $$
then the answer is infinite with probability |
4,061 | Reference book for linear algebra applied to statistics? | The "big three" that I have used/heard of are:
Gentle, Matrix Algebra: Theory, Computations, and Applications in Statistics. (Amazon link).
Searle, Matrix Algebra Useful for Statistics. (Amazon link).
Harville, Matrix Algebra From a Statistician's Perspective. (Amazon link).
I have used Gentle and Harville and found b... | Reference book for linear algebra applied to statistics? | The "big three" that I have used/heard of are:
Gentle, Matrix Algebra: Theory, Computations, and Applications in Statistics. (Amazon link).
Searle, Matrix Algebra Useful for Statistics. (Amazon link) | Reference book for linear algebra applied to statistics?
The "big three" that I have used/heard of are:
Gentle, Matrix Algebra: Theory, Computations, and Applications in Statistics. (Amazon link).
Searle, Matrix Algebra Useful for Statistics. (Amazon link).
Harville, Matrix Algebra From a Statistician's Perspective. (... | Reference book for linear algebra applied to statistics?
The "big three" that I have used/heard of are:
Gentle, Matrix Algebra: Theory, Computations, and Applications in Statistics. (Amazon link).
Searle, Matrix Algebra Useful for Statistics. (Amazon link) |
4,062 | Reference book for linear algebra applied to statistics? | The Matrix Cookbook by K. B. Petersen.
is a free resource will all sorts of useful identities involving various decompositions, forms of inverses for various commonly encountered matrix structures, formulas for differentiating matrix functions and much more. You'll probably find whatever you're looking for in the matr... | Reference book for linear algebra applied to statistics? | The Matrix Cookbook by K. B. Petersen.
is a free resource will all sorts of useful identities involving various decompositions, forms of inverses for various commonly encountered matrix structures, f | Reference book for linear algebra applied to statistics?
The Matrix Cookbook by K. B. Petersen.
is a free resource will all sorts of useful identities involving various decompositions, forms of inverses for various commonly encountered matrix structures, formulas for differentiating matrix functions and much more. You... | Reference book for linear algebra applied to statistics?
The Matrix Cookbook by K. B. Petersen.
is a free resource will all sorts of useful identities involving various decompositions, forms of inverses for various commonly encountered matrix structures, f |
4,063 | Reference book for linear algebra applied to statistics? | Matrix Computations by Golub and Van Loan is the standard reference for matrix computation for many. | Reference book for linear algebra applied to statistics? | Matrix Computations by Golub and Van Loan is the standard reference for matrix computation for many. | Reference book for linear algebra applied to statistics?
Matrix Computations by Golub and Van Loan is the standard reference for matrix computation for many. | Reference book for linear algebra applied to statistics?
Matrix Computations by Golub and Van Loan is the standard reference for matrix computation for many. |
4,064 | Reference book for linear algebra applied to statistics? | I've found Advanced Multivariate Statistics with Matrices by Kollo and von Rosen to be very useful when working with multivariate statistics. The first 170 pages are linear algebra. It then goes on to cover multivariate distributions, asymptotics and linear models - all in a rigorous way. It doesn't cover projection me... | Reference book for linear algebra applied to statistics? | I've found Advanced Multivariate Statistics with Matrices by Kollo and von Rosen to be very useful when working with multivariate statistics. The first 170 pages are linear algebra. It then goes on to | Reference book for linear algebra applied to statistics?
I've found Advanced Multivariate Statistics with Matrices by Kollo and von Rosen to be very useful when working with multivariate statistics. The first 170 pages are linear algebra. It then goes on to cover multivariate distributions, asymptotics and linear model... | Reference book for linear algebra applied to statistics?
I've found Advanced Multivariate Statistics with Matrices by Kollo and von Rosen to be very useful when working with multivariate statistics. The first 170 pages are linear algebra. It then goes on to |
4,065 | Reference book for linear algebra applied to statistics? | In addition to the three mentioned by @Mike Wierzbicki (all of which I use), another useful one is "Matrix Tricks for Linear Statistical Models" by Puntanen, Styan and Isotalo (2011). | Reference book for linear algebra applied to statistics? | In addition to the three mentioned by @Mike Wierzbicki (all of which I use), another useful one is "Matrix Tricks for Linear Statistical Models" by Puntanen, Styan and Isotalo (2011). | Reference book for linear algebra applied to statistics?
In addition to the three mentioned by @Mike Wierzbicki (all of which I use), another useful one is "Matrix Tricks for Linear Statistical Models" by Puntanen, Styan and Isotalo (2011). | Reference book for linear algebra applied to statistics?
In addition to the three mentioned by @Mike Wierzbicki (all of which I use), another useful one is "Matrix Tricks for Linear Statistical Models" by Puntanen, Styan and Isotalo (2011). |
4,066 | Reference book for linear algebra applied to statistics? | You could try "Numerical Methods of Statistics", by John F. Monahan. It assumes that you know linear algebra, but the author's web site provides programs coded in R. | Reference book for linear algebra applied to statistics? | You could try "Numerical Methods of Statistics", by John F. Monahan. It assumes that you know linear algebra, but the author's web site provides programs coded in R. | Reference book for linear algebra applied to statistics?
You could try "Numerical Methods of Statistics", by John F. Monahan. It assumes that you know linear algebra, but the author's web site provides programs coded in R. | Reference book for linear algebra applied to statistics?
You could try "Numerical Methods of Statistics", by John F. Monahan. It assumes that you know linear algebra, but the author's web site provides programs coded in R. |
4,067 | Reference book for linear algebra applied to statistics? | Krishnan Namboodiri's Matrix Algebra: An Introduction is a quick, bare-bones way to learn much of the linear algebra you'll need.
You can also try MIT OCW. | Reference book for linear algebra applied to statistics? | Krishnan Namboodiri's Matrix Algebra: An Introduction is a quick, bare-bones way to learn much of the linear algebra you'll need.
You can also try MIT OCW. | Reference book for linear algebra applied to statistics?
Krishnan Namboodiri's Matrix Algebra: An Introduction is a quick, bare-bones way to learn much of the linear algebra you'll need.
You can also try MIT OCW. | Reference book for linear algebra applied to statistics?
Krishnan Namboodiri's Matrix Algebra: An Introduction is a quick, bare-bones way to learn much of the linear algebra you'll need.
You can also try MIT OCW. |
4,068 | Reference book for linear algebra applied to statistics? | I have Anton's Elementary Linear Algebra, mainly for the chapters on linear equations and matrices and on determinants (I have the 7th edition). | Reference book for linear algebra applied to statistics? | I have Anton's Elementary Linear Algebra, mainly for the chapters on linear equations and matrices and on determinants (I have the 7th edition). | Reference book for linear algebra applied to statistics?
I have Anton's Elementary Linear Algebra, mainly for the chapters on linear equations and matrices and on determinants (I have the 7th edition). | Reference book for linear algebra applied to statistics?
I have Anton's Elementary Linear Algebra, mainly for the chapters on linear equations and matrices and on determinants (I have the 7th edition). |
4,069 | Reference book for linear algebra applied to statistics? | As a mathematical statistics student Rencher's book named Linear Models In Statistics was very helpful for me, especially in working with mean and variance of quadratic forms. It is available in this link. I hope it could be useful for other students and researchers too. | Reference book for linear algebra applied to statistics? | As a mathematical statistics student Rencher's book named Linear Models In Statistics was very helpful for me, especially in working with mean and variance of quadratic forms. It is available in this | Reference book for linear algebra applied to statistics?
As a mathematical statistics student Rencher's book named Linear Models In Statistics was very helpful for me, especially in working with mean and variance of quadratic forms. It is available in this link. I hope it could be useful for other students and research... | Reference book for linear algebra applied to statistics?
As a mathematical statistics student Rencher's book named Linear Models In Statistics was very helpful for me, especially in working with mean and variance of quadratic forms. It is available in this |
4,070 | Reference book for linear algebra applied to statistics? | It doesn't advertise itself as "for statisticians", but many statisticians have made great use of Gil Strang's Intro to Linear Algebra, which covers all the topics you describe, and has chapters about statistical applications. | Reference book for linear algebra applied to statistics? | It doesn't advertise itself as "for statisticians", but many statisticians have made great use of Gil Strang's Intro to Linear Algebra, which covers all the topics you describe, and has chapters about | Reference book for linear algebra applied to statistics?
It doesn't advertise itself as "for statisticians", but many statisticians have made great use of Gil Strang's Intro to Linear Algebra, which covers all the topics you describe, and has chapters about statistical applications. | Reference book for linear algebra applied to statistics?
It doesn't advertise itself as "for statisticians", but many statisticians have made great use of Gil Strang's Intro to Linear Algebra, which covers all the topics you describe, and has chapters about |
4,071 | Reference book for linear algebra applied to statistics? | Mathematics for Machine Learning is another nice alternative (freely available) | Reference book for linear algebra applied to statistics? | Mathematics for Machine Learning is another nice alternative (freely available) | Reference book for linear algebra applied to statistics?
Mathematics for Machine Learning is another nice alternative (freely available) | Reference book for linear algebra applied to statistics?
Mathematics for Machine Learning is another nice alternative (freely available) |
4,072 | Reference book for linear algebra applied to statistics? | I second many of the books recommended, especially Rencher's book Linear Models In Statistics. Another book I would recommend is Hands-On Matrix Algebra Using R: Active And Motivated Learning With Applications (amazon link). It is not overly technical, and provides many examples in R, which I found useful when learning... | Reference book for linear algebra applied to statistics? | I second many of the books recommended, especially Rencher's book Linear Models In Statistics. Another book I would recommend is Hands-On Matrix Algebra Using R: Active And Motivated Learning With App | Reference book for linear algebra applied to statistics?
I second many of the books recommended, especially Rencher's book Linear Models In Statistics. Another book I would recommend is Hands-On Matrix Algebra Using R: Active And Motivated Learning With Applications (amazon link). It is not overly technical, and provid... | Reference book for linear algebra applied to statistics?
I second many of the books recommended, especially Rencher's book Linear Models In Statistics. Another book I would recommend is Hands-On Matrix Algebra Using R: Active And Motivated Learning With App |
4,073 | How should one interpret the comparison of means from different sample sizes? | You can use a t-test to assess if there are differences in the means. The different sample sizes don't cause a problem for the t-test, and don't require the results to be interpreted with any extra care. Ultimately, you can even compare a single observation to an infinite population with a known distribution and mean... | How should one interpret the comparison of means from different sample sizes? | You can use a t-test to assess if there are differences in the means. The different sample sizes don't cause a problem for the t-test, and don't require the results to be interpreted with any extra c | How should one interpret the comparison of means from different sample sizes?
You can use a t-test to assess if there are differences in the means. The different sample sizes don't cause a problem for the t-test, and don't require the results to be interpreted with any extra care. Ultimately, you can even compare a s... | How should one interpret the comparison of means from different sample sizes?
You can use a t-test to assess if there are differences in the means. The different sample sizes don't cause a problem for the t-test, and don't require the results to be interpreted with any extra c |
4,074 | How should one interpret the comparison of means from different sample sizes? | In addition to the answer mentioned by @gung referring you to the t-test, it sounds like you might be interested in Bayesian rating systems. Websites can use such systems to rank order items that vary in the number of votes received. Essentially, such systems work by assigning a rating that is a composite of the mean r... | How should one interpret the comparison of means from different sample sizes? | In addition to the answer mentioned by @gung referring you to the t-test, it sounds like you might be interested in Bayesian rating systems. Websites can use such systems to rank order items that vary | How should one interpret the comparison of means from different sample sizes?
In addition to the answer mentioned by @gung referring you to the t-test, it sounds like you might be interested in Bayesian rating systems. Websites can use such systems to rank order items that vary in the number of votes received. Essentia... | How should one interpret the comparison of means from different sample sizes?
In addition to the answer mentioned by @gung referring you to the t-test, it sounds like you might be interested in Bayesian rating systems. Websites can use such systems to rank order items that vary |
4,075 | Period detection of a generic time series | If you really have no idea what the periodicity is, probably the best approach is to find the frequency corresponding to the maximum of the spectral density. However, the spectrum at low frequencies will be affected by trend, so you need to detrend the series first. The following R function should do the job for most s... | Period detection of a generic time series | If you really have no idea what the periodicity is, probably the best approach is to find the frequency corresponding to the maximum of the spectral density. However, the spectrum at low frequencies w | Period detection of a generic time series
If you really have no idea what the periodicity is, probably the best approach is to find the frequency corresponding to the maximum of the spectral density. However, the spectrum at low frequencies will be affected by trend, so you need to detrend the series first. The followi... | Period detection of a generic time series
If you really have no idea what the periodicity is, probably the best approach is to find the frequency corresponding to the maximum of the spectral density. However, the spectrum at low frequencies w |
4,076 | Period detection of a generic time series | If you expect the process to be stationary -- the periodicity/seasonality will not change over time -- then something like a Chi-square periodogram (see e.g. Sokolove and Bushell, 1978) might be a good choice. It's commonly used in analysis of circadian data which can have extremely large amounts of noise in it, but i... | Period detection of a generic time series | If you expect the process to be stationary -- the periodicity/seasonality will not change over time -- then something like a Chi-square periodogram (see e.g. Sokolove and Bushell, 1978) might be a goo | Period detection of a generic time series
If you expect the process to be stationary -- the periodicity/seasonality will not change over time -- then something like a Chi-square periodogram (see e.g. Sokolove and Bushell, 1978) might be a good choice. It's commonly used in analysis of circadian data which can have ext... | Period detection of a generic time series
If you expect the process to be stationary -- the periodicity/seasonality will not change over time -- then something like a Chi-square periodogram (see e.g. Sokolove and Bushell, 1978) might be a goo |
4,077 | Period detection of a generic time series | You may want to define what you want more clearly (to yourself, if not here). If what you're looking for is the most statistically significant stationary period contained in your noisy data, there's essentially two routes to take:
1) compute a robust autocorrelation estimate, and take the maximum coefficient
2) compute... | Period detection of a generic time series | You may want to define what you want more clearly (to yourself, if not here). If what you're looking for is the most statistically significant stationary period contained in your noisy data, there's e | Period detection of a generic time series
You may want to define what you want more clearly (to yourself, if not here). If what you're looking for is the most statistically significant stationary period contained in your noisy data, there's essentially two routes to take:
1) compute a robust autocorrelation estimate, a... | Period detection of a generic time series
You may want to define what you want more clearly (to yourself, if not here). If what you're looking for is the most statistically significant stationary period contained in your noisy data, there's e |
4,078 | Period detection of a generic time series | You could use the Hilbert Transformation from DSP theory to measure the instantaneous frequency of your data. The site http://ta-lib.org/ has open source code for measuring the dominant cycle period of financial data; the relevant function is called HT_DCPERIOD; you might be able to use this or adapt the code to your p... | Period detection of a generic time series | You could use the Hilbert Transformation from DSP theory to measure the instantaneous frequency of your data. The site http://ta-lib.org/ has open source code for measuring the dominant cycle period o | Period detection of a generic time series
You could use the Hilbert Transformation from DSP theory to measure the instantaneous frequency of your data. The site http://ta-lib.org/ has open source code for measuring the dominant cycle period of financial data; the relevant function is called HT_DCPERIOD; you might be ab... | Period detection of a generic time series
You could use the Hilbert Transformation from DSP theory to measure the instantaneous frequency of your data. The site http://ta-lib.org/ has open source code for measuring the dominant cycle period o |
4,079 | Period detection of a generic time series | A different approach could be Empirical Mode Decomposition. The R package is called EMD developed by the inventor of the method:
require(EMD)
ndata <- 3000
tt2 <- seq(0, 9, length = ndata)
xt2 <- sin(pi * tt2) + sin(2* pi * tt2) + sin(6 * pi * tt2) + 0.5 * tt2
try <- emd(xt2, tt2, boundary = "wave")
### Ploting... | Period detection of a generic time series | A different approach could be Empirical Mode Decomposition. The R package is called EMD developed by the inventor of the method:
require(EMD)
ndata <- 3000
tt2 <- seq(0, 9, length = ndata)
xt2 <- | Period detection of a generic time series
A different approach could be Empirical Mode Decomposition. The R package is called EMD developed by the inventor of the method:
require(EMD)
ndata <- 3000
tt2 <- seq(0, 9, length = ndata)
xt2 <- sin(pi * tt2) + sin(2* pi * tt2) + sin(6 * pi * tt2) + 0.5 * tt2
try <- emd(... | Period detection of a generic time series
A different approach could be Empirical Mode Decomposition. The R package is called EMD developed by the inventor of the method:
require(EMD)
ndata <- 3000
tt2 <- seq(0, 9, length = ndata)
xt2 <- |
4,080 | Period detection of a generic time series | In reference to Rob Hyndman's post above https://stats.stackexchange.com/a/1214/70282
The find.freq function works brilliantly. On the daily data set I am using, it correctly worked out the frequency to be 7.
When I tried it on only the week days, it mentioned the frequency is 23, which is remarkably close to 21.42857=... | Period detection of a generic time series | In reference to Rob Hyndman's post above https://stats.stackexchange.com/a/1214/70282
The find.freq function works brilliantly. On the daily data set I am using, it correctly worked out the frequency | Period detection of a generic time series
In reference to Rob Hyndman's post above https://stats.stackexchange.com/a/1214/70282
The find.freq function works brilliantly. On the daily data set I am using, it correctly worked out the frequency to be 7.
When I tried it on only the week days, it mentioned the frequency is ... | Period detection of a generic time series
In reference to Rob Hyndman's post above https://stats.stackexchange.com/a/1214/70282
The find.freq function works brilliantly. On the daily data set I am using, it correctly worked out the frequency |
4,081 | Period detection of a generic time series | One can also use Ljung-Box test to figure out which seasonal difference reaches to best stationarity. I was working on a different subject and I used this actually for the same purposes. Try different periods such as 3 to 24 for a monthly data. And test each of them by Ljung-Box and store Chi-Square results. And choose... | Period detection of a generic time series | One can also use Ljung-Box test to figure out which seasonal difference reaches to best stationarity. I was working on a different subject and I used this actually for the same purposes. Try different | Period detection of a generic time series
One can also use Ljung-Box test to figure out which seasonal difference reaches to best stationarity. I was working on a different subject and I used this actually for the same purposes. Try different periods such as 3 to 24 for a monthly data. And test each of them by Ljung-Bo... | Period detection of a generic time series
One can also use Ljung-Box test to figure out which seasonal difference reaches to best stationarity. I was working on a different subject and I used this actually for the same purposes. Try different |
4,082 | Recurrent vs Recursive Neural Networks: Which is better for NLP? | Recurrent Neural networks are recurring over time. For example if you have a sequence
x = ['h', 'e', 'l', 'l']
This sequence is fed to a single neuron which has a single connection to itself.
At time step 0, the letter 'h' is given as input.At time step 1, 'e' is given as input. The network when unfolded over time will... | Recurrent vs Recursive Neural Networks: Which is better for NLP? | Recurrent Neural networks are recurring over time. For example if you have a sequence
x = ['h', 'e', 'l', 'l']
This sequence is fed to a single neuron which has a single connection to itself.
At time | Recurrent vs Recursive Neural Networks: Which is better for NLP?
Recurrent Neural networks are recurring over time. For example if you have a sequence
x = ['h', 'e', 'l', 'l']
This sequence is fed to a single neuron which has a single connection to itself.
At time step 0, the letter 'h' is given as input.At time step 1... | Recurrent vs Recursive Neural Networks: Which is better for NLP?
Recurrent Neural networks are recurring over time. For example if you have a sequence
x = ['h', 'e', 'l', 'l']
This sequence is fed to a single neuron which has a single connection to itself.
At time |
4,083 | Recurrent vs Recursive Neural Networks: Which is better for NLP? | Large Recurrent Neural Networks are considered maybe the most powerful model for NLP. A great article written by A. Karpathy on Recurrent Neural Networks and character level modeling is available at http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Having tried a large number of libraries for deep learning (thean... | Recurrent vs Recursive Neural Networks: Which is better for NLP? | Large Recurrent Neural Networks are considered maybe the most powerful model for NLP. A great article written by A. Karpathy on Recurrent Neural Networks and character level modeling is available at h | Recurrent vs Recursive Neural Networks: Which is better for NLP?
Large Recurrent Neural Networks are considered maybe the most powerful model for NLP. A great article written by A. Karpathy on Recurrent Neural Networks and character level modeling is available at http://karpathy.github.io/2015/05/21/rnn-effectiveness/
... | Recurrent vs Recursive Neural Networks: Which is better for NLP?
Large Recurrent Neural Networks are considered maybe the most powerful model for NLP. A great article written by A. Karpathy on Recurrent Neural Networks and character level modeling is available at h |
4,084 | Recurrent vs Recursive Neural Networks: Which is better for NLP? | Recurrent Neural Networks (RNN) basically unfolds over time. It is used for sequential inputs where the time factor is the main differentiating factor between the elements of the sequence. For example, here is a recurrent neural network used for language modeling that has been unfolded over time. At each time step, in ... | Recurrent vs Recursive Neural Networks: Which is better for NLP? | Recurrent Neural Networks (RNN) basically unfolds over time. It is used for sequential inputs where the time factor is the main differentiating factor between the elements of the sequence. For example | Recurrent vs Recursive Neural Networks: Which is better for NLP?
Recurrent Neural Networks (RNN) basically unfolds over time. It is used for sequential inputs where the time factor is the main differentiating factor between the elements of the sequence. For example, here is a recurrent neural network used for language ... | Recurrent vs Recursive Neural Networks: Which is better for NLP?
Recurrent Neural Networks (RNN) basically unfolds over time. It is used for sequential inputs where the time factor is the main differentiating factor between the elements of the sequence. For example |
4,085 | Recurrent vs Recursive Neural Networks: Which is better for NLP? | To answer a couple of the questions:
CNNs definitely are used for NLP tasks sometimes. They are one way to take a variable-length natural language input and reduce it to a fixed length output such as a sentence embedding. Google's Multilingual Universal Sentence Encoder (USE) is one example:
https://arxiv.org/abs/... | Recurrent vs Recursive Neural Networks: Which is better for NLP? | To answer a couple of the questions:
CNNs definitely are used for NLP tasks sometimes. They are one way to take a variable-length natural language input and reduce it to a fixed length output such as | Recurrent vs Recursive Neural Networks: Which is better for NLP?
To answer a couple of the questions:
CNNs definitely are used for NLP tasks sometimes. They are one way to take a variable-length natural language input and reduce it to a fixed length output such as a sentence embedding. Google's Multilingual Universal... | Recurrent vs Recursive Neural Networks: Which is better for NLP?
To answer a couple of the questions:
CNNs definitely are used for NLP tasks sometimes. They are one way to take a variable-length natural language input and reduce it to a fixed length output such as |
4,086 | What is a difference between random effects-, fixed effects- and marginal model? | This question has been partially discussed at this site as below, and opinions seem mixed.
What is the difference between fixed effect, random effect and mixed effect models?
What is the mathematical difference between random- and fixed-effects?
Concepts behind fixed/random effects models
All terms are generally rela... | What is a difference between random effects-, fixed effects- and marginal model? | This question has been partially discussed at this site as below, and opinions seem mixed.
What is the difference between fixed effect, random effect and mixed effect models?
What is the mathematical | What is a difference between random effects-, fixed effects- and marginal model?
This question has been partially discussed at this site as below, and opinions seem mixed.
What is the difference between fixed effect, random effect and mixed effect models?
What is the mathematical difference between random- and fixed-e... | What is a difference between random effects-, fixed effects- and marginal model?
This question has been partially discussed at this site as below, and opinions seem mixed.
What is the difference between fixed effect, random effect and mixed effect models?
What is the mathematical |
4,087 | What is a difference between random effects-, fixed effects- and marginal model? | Correct me if I'm wrong here:
Conceptually, there are four possible effects: Fixed intercept, fixed coefficient, random intercept, random coefficient. Most regression models are 'random effects', so they have random intercepts and random coefficients. The term 'random effect' came into use in contrast to 'fixed effect... | What is a difference between random effects-, fixed effects- and marginal model? | Correct me if I'm wrong here:
Conceptually, there are four possible effects: Fixed intercept, fixed coefficient, random intercept, random coefficient. Most regression models are 'random effects', so | What is a difference between random effects-, fixed effects- and marginal model?
Correct me if I'm wrong here:
Conceptually, there are four possible effects: Fixed intercept, fixed coefficient, random intercept, random coefficient. Most regression models are 'random effects', so they have random intercepts and random ... | What is a difference between random effects-, fixed effects- and marginal model?
Correct me if I'm wrong here:
Conceptually, there are four possible effects: Fixed intercept, fixed coefficient, random intercept, random coefficient. Most regression models are 'random effects', so |
4,088 | How to apply standardization/normalization to train- and testset if prediction is the goal? | The third way is correct. Exactly why is covered in wonderful detail in The Elements of Statistical Learning, see the section "The Wrong and Right Way to Do Cross-validation", and also in the final chapter of Learning From Data, in the stock market example.
Essentially, procedures 1 and 2 leak information about either... | How to apply standardization/normalization to train- and testset if prediction is the goal? | The third way is correct. Exactly why is covered in wonderful detail in The Elements of Statistical Learning, see the section "The Wrong and Right Way to Do Cross-validation", and also in the final c | How to apply standardization/normalization to train- and testset if prediction is the goal?
The third way is correct. Exactly why is covered in wonderful detail in The Elements of Statistical Learning, see the section "The Wrong and Right Way to Do Cross-validation", and also in the final chapter of Learning From Data... | How to apply standardization/normalization to train- and testset if prediction is the goal?
The third way is correct. Exactly why is covered in wonderful detail in The Elements of Statistical Learning, see the section "The Wrong and Right Way to Do Cross-validation", and also in the final c |
4,089 | What is the root cause of the class imbalance problem? | An entry from the Encyclopedia of Machine Learning (https://cling.csd.uwo.ca/papers/cost_sensitive.pdf) helpfully explains that what gets called "the class imbalance problem" is better understood as three separate problems:
assuming that an accuracy metric is appropriate when it is not
assuming that the test distribu... | What is the root cause of the class imbalance problem? | An entry from the Encyclopedia of Machine Learning (https://cling.csd.uwo.ca/papers/cost_sensitive.pdf) helpfully explains that what gets called "the class imbalance problem" is better understood as t | What is the root cause of the class imbalance problem?
An entry from the Encyclopedia of Machine Learning (https://cling.csd.uwo.ca/papers/cost_sensitive.pdf) helpfully explains that what gets called "the class imbalance problem" is better understood as three separate problems:
assuming that an accuracy metric is app... | What is the root cause of the class imbalance problem?
An entry from the Encyclopedia of Machine Learning (https://cling.csd.uwo.ca/papers/cost_sensitive.pdf) helpfully explains that what gets called "the class imbalance problem" is better understood as t |
4,090 | What is the root cause of the class imbalance problem? | Anything that involves optimization to minimize a loss function will, if sufficiently convex, give a solution that is a global minimum of that loss function. I say 'sufficiently convex' since deep networks are not on the whole convex, but give reasonable minimums in practice, with careful choices of learning rate etc.... | What is the root cause of the class imbalance problem? | Anything that involves optimization to minimize a loss function will, if sufficiently convex, give a solution that is a global minimum of that loss function. I say 'sufficiently convex' since deep ne | What is the root cause of the class imbalance problem?
Anything that involves optimization to minimize a loss function will, if sufficiently convex, give a solution that is a global minimum of that loss function. I say 'sufficiently convex' since deep networks are not on the whole convex, but give reasonable minimums ... | What is the root cause of the class imbalance problem?
Anything that involves optimization to minimize a loss function will, if sufficiently convex, give a solution that is a global minimum of that loss function. I say 'sufficiently convex' since deep ne |
4,091 | What is the root cause of the class imbalance problem? | Note that one-class classifiers don't have an imbalance problem as they look at each class independently from all other classes and they can cope with "not-classes" by just not modeling them. (They may have a problem with too small sample size, of course).
Many problems that would be more appropriately modeled by one-... | What is the root cause of the class imbalance problem? | Note that one-class classifiers don't have an imbalance problem as they look at each class independently from all other classes and they can cope with "not-classes" by just not modeling them. (They ma | What is the root cause of the class imbalance problem?
Note that one-class classifiers don't have an imbalance problem as they look at each class independently from all other classes and they can cope with "not-classes" by just not modeling them. (They may have a problem with too small sample size, of course).
Many pr... | What is the root cause of the class imbalance problem?
Note that one-class classifiers don't have an imbalance problem as they look at each class independently from all other classes and they can cope with "not-classes" by just not modeling them. (They ma |
4,092 | What is the root cause of the class imbalance problem? | Tongue slightly in cheek - the root cause of the class imbalance problem is calling it the class imbalance problem, which implies that the class imbalance causes a problem. This is very rarely the case (and when it does happen the only solution is likely to be to collect more data). The real problem is practitioners ... | What is the root cause of the class imbalance problem? | Tongue slightly in cheek - the root cause of the class imbalance problem is calling it the class imbalance problem, which implies that the class imbalance causes a problem. This is very rarely the ca | What is the root cause of the class imbalance problem?
Tongue slightly in cheek - the root cause of the class imbalance problem is calling it the class imbalance problem, which implies that the class imbalance causes a problem. This is very rarely the case (and when it does happen the only solution is likely to be to ... | What is the root cause of the class imbalance problem?
Tongue slightly in cheek - the root cause of the class imbalance problem is calling it the class imbalance problem, which implies that the class imbalance causes a problem. This is very rarely the ca |
4,093 | Bootstrap vs. jackknife | Bootstrapping is a superior technique and can be used pretty much anywhere jackknifing has been used. Jackknifing is much older (perhaps ~20 years); it's main advantage in the days when computing power was limited, was that it's computationally much simpler. However, the bootstrap provides information about the whole... | Bootstrap vs. jackknife | Bootstrapping is a superior technique and can be used pretty much anywhere jackknifing has been used. Jackknifing is much older (perhaps ~20 years); it's main advantage in the days when computing pow | Bootstrap vs. jackknife
Bootstrapping is a superior technique and can be used pretty much anywhere jackknifing has been used. Jackknifing is much older (perhaps ~20 years); it's main advantage in the days when computing power was limited, was that it's computationally much simpler. However, the bootstrap provides inf... | Bootstrap vs. jackknife
Bootstrapping is a superior technique and can be used pretty much anywhere jackknifing has been used. Jackknifing is much older (perhaps ~20 years); it's main advantage in the days when computing pow |
4,094 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | By way of background, I have been doing forecasting store $\times$ SKU time series for retail sales for 12 years now. Tens of thousands of time series across hundreds or thousands of stores. I like saying that we have been doing Big Data since before the term became popular.
I have consistently found that the single mo... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | By way of background, I have been doing forecasting store $\times$ SKU time series for retail sales for 12 years now. Tens of thousands of time series across hundreds or thousands of stores. I like sa | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
By way of background, I have been doing forecasting store $\times$ SKU time series for retail sales for 12 years now. Tens of thousands of time series across hundreds or thousands of s... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
By way of background, I have been doing forecasting store $\times$ SKU time series for retail sales for 12 years now. Tens of thousands of time series across hundreds or thousands of stores. I like sa |
4,095 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | I can't speak for the whole of industry, obviously, but I work in industry and have competed on Kaggle so I will share my POV.
First, you're right to suspect that Kaggle doesn't exactly match what people are doing in industry. It's a game, and subject to gamesmanship, with lots of crazy restrictions. For example, in th... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | I can't speak for the whole of industry, obviously, but I work in industry and have competed on Kaggle so I will share my POV.
First, you're right to suspect that Kaggle doesn't exactly match what peo | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
I can't speak for the whole of industry, obviously, but I work in industry and have competed on Kaggle so I will share my POV.
First, you're right to suspect that Kaggle doesn't exactl... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
I can't speak for the whole of industry, obviously, but I work in industry and have competed on Kaggle so I will share my POV.
First, you're right to suspect that Kaggle doesn't exactly match what peo |
4,096 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | From my experience, more data and more features are more important than the fanciest, most stacked, most tuned, model one can come up with.
Look at the online advertising competitions that took place. Winning models were so complex they ended up taking a whole week to train (on a very small dataset, compared to the ind... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | From my experience, more data and more features are more important than the fanciest, most stacked, most tuned, model one can come up with.
Look at the online advertising competitions that took place. | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
From my experience, more data and more features are more important than the fanciest, most stacked, most tuned, model one can come up with.
Look at the online advertising competitions ... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
From my experience, more data and more features are more important than the fanciest, most stacked, most tuned, model one can come up with.
Look at the online advertising competitions that took place. |
4,097 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | Stacking significantly increases complexity and reduces interpretability. The gains are usually relatively small to justify it. So while ensembling is probably widely used (e.g. XGBoost), I think stacking is relatively rare in industry. | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | Stacking significantly increases complexity and reduces interpretability. The gains are usually relatively small to justify it. So while ensembling is probably widely used (e.g. XGBoost), I think stac | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
Stacking significantly increases complexity and reduces interpretability. The gains are usually relatively small to justify it. So while ensembling is probably widely used (e.g. XGBoos... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
Stacking significantly increases complexity and reduces interpretability. The gains are usually relatively small to justify it. So while ensembling is probably widely used (e.g. XGBoost), I think stac |
4,098 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | In my experience collecting good data and features is much more important.
The clients we worked with usually have a lot of data, and not all of it in format that can be readily exported or easy to work with. The first batch of data is usually not very useful; it is our task to work with the client to figure what data ... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | In my experience collecting good data and features is much more important.
The clients we worked with usually have a lot of data, and not all of it in format that can be readily exported or easy to wo | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
In my experience collecting good data and features is much more important.
The clients we worked with usually have a lot of data, and not all of it in format that can be readily export... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
In my experience collecting good data and features is much more important.
The clients we worked with usually have a lot of data, and not all of it in format that can be readily exported or easy to wo |
4,099 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | Here's something that doesn't come up much on Kaggle: the
more variables you have in your model, and
the more complex the relationship between those variables and the output,
the more risk you will face over the lifetime of that model. Time is typically either frozen in Kaggle competitions, or there's a short future... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | Here's something that doesn't come up much on Kaggle: the
more variables you have in your model, and
the more complex the relationship between those variables and the output,
the more risk you will | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
Here's something that doesn't come up much on Kaggle: the
more variables you have in your model, and
the more complex the relationship between those variables and the output,
the mo... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
Here's something that doesn't come up much on Kaggle: the
more variables you have in your model, and
the more complex the relationship between those variables and the output,
the more risk you will |
4,100 | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling? | A short answer which is a quote I like from Gary Kasparov's book Deep Thinking
A clever process beats superior knowledge and superior technology
I work mainly with time-series financial data, and the process from gathering data, cleaning it, processing it, and then working with the problem owners to figure out what t... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m | A short answer which is a quote I like from Gary Kasparov's book Deep Thinking
A clever process beats superior knowledge and superior technology
I work mainly with time-series financial data, and th | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?
A short answer which is a quote I like from Gary Kasparov's book Deep Thinking
A clever process beats superior knowledge and superior technology
I work mainly with time-series financ... | Industry vs Kaggle challenges. Is collecting more observations and having access to more variables m
A short answer which is a quote I like from Gary Kasparov's book Deep Thinking
A clever process beats superior knowledge and superior technology
I work mainly with time-series financial data, and th |
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