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 |
|---|---|---|---|---|---|---|
13,401 | When can we speak of collinearity | A common way to evaluate collinearity is with variance inflation factors (VIFs). This can be achieved in R using the 'vif' function within the 'car' package. This has an advantage over looking at only the correlations between two variables, as it simultaneously evaluates the correlation between one variable and the res... | When can we speak of collinearity | A common way to evaluate collinearity is with variance inflation factors (VIFs). This can be achieved in R using the 'vif' function within the 'car' package. This has an advantage over looking at only | When can we speak of collinearity
A common way to evaluate collinearity is with variance inflation factors (VIFs). This can be achieved in R using the 'vif' function within the 'car' package. This has an advantage over looking at only the correlations between two variables, as it simultaneously evaluates the correlatio... | When can we speak of collinearity
A common way to evaluate collinearity is with variance inflation factors (VIFs). This can be achieved in R using the 'vif' function within the 'car' package. This has an advantage over looking at only |
13,402 | Gaussian RBF vs. Gaussian kernel | The only real difference is in the regularisation that is applied. A regularised RBF network typically uses a penalty based on the squared norm of the weights. For the kernel version, the penalty is typically on the squared norm of the weights of the linear model implicitly constructed in the feature space induced by... | Gaussian RBF vs. Gaussian kernel | The only real difference is in the regularisation that is applied. A regularised RBF network typically uses a penalty based on the squared norm of the weights. For the kernel version, the penalty is | Gaussian RBF vs. Gaussian kernel
The only real difference is in the regularisation that is applied. A regularised RBF network typically uses a penalty based on the squared norm of the weights. For the kernel version, the penalty is typically on the squared norm of the weights of the linear model implicitly constructe... | Gaussian RBF vs. Gaussian kernel
The only real difference is in the regularisation that is applied. A regularised RBF network typically uses a penalty based on the squared norm of the weights. For the kernel version, the penalty is |
13,403 | When is distance covariance less appropriate than linear covariance? | I have tried to collect a few remarks on distance covariance based on my impressions from reading the references listed below. However, I do not consider myself an
expert on this topic. Comments, corrections, suggestions, etc. are welcome.
The remarks are (strongly) biased towards
potential drawbacks, as requested in t... | When is distance covariance less appropriate than linear covariance? | I have tried to collect a few remarks on distance covariance based on my impressions from reading the references listed below. However, I do not consider myself an
expert on this topic. Comments, corr | When is distance covariance less appropriate than linear covariance?
I have tried to collect a few remarks on distance covariance based on my impressions from reading the references listed below. However, I do not consider myself an
expert on this topic. Comments, corrections, suggestions, etc. are welcome.
The remarks... | When is distance covariance less appropriate than linear covariance?
I have tried to collect a few remarks on distance covariance based on my impressions from reading the references listed below. However, I do not consider myself an
expert on this topic. Comments, corr |
13,404 | When is distance covariance less appropriate than linear covariance? | I could well be missing something, but just having a quantification of the nonlinear dependence between two variables doesn't seem to have much of a payoff. It won't tell you the shape of the relationship. It won't give you any means to predict one variable from the other. By analogy, when doing exploratory data ana... | When is distance covariance less appropriate than linear covariance? | I could well be missing something, but just having a quantification of the nonlinear dependence between two variables doesn't seem to have much of a payoff. It won't tell you the shape of the relatio | When is distance covariance less appropriate than linear covariance?
I could well be missing something, but just having a quantification of the nonlinear dependence between two variables doesn't seem to have much of a payoff. It won't tell you the shape of the relationship. It won't give you any means to predict one ... | When is distance covariance less appropriate than linear covariance?
I could well be missing something, but just having a quantification of the nonlinear dependence between two variables doesn't seem to have much of a payoff. It won't tell you the shape of the relatio |
13,405 | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research? | A quick response to the bulleted content:
1) Power / Type 1 error in a Bayesian analysis vs. a frequentist analysis
Asking about Type 1 and power (i.e. one minus the probability of Type 2 error) implies that you can put your inference problem into a repeated sampling framework. Can you? If you can't then there isn't ... | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavi | A quick response to the bulleted content:
1) Power / Type 1 error in a Bayesian analysis vs. a frequentist analysis
Asking about Type 1 and power (i.e. one minus the probability of Type 2 error) impli | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research?
A quick response to the bulleted content:
1) Power / Type 1 error in a Bayesian analysis vs. a frequentist analysis
Asking about Type 1 and power (i.e. one minus the probability of Type 2 error) implies th... | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavi
A quick response to the bulleted content:
1) Power / Type 1 error in a Bayesian analysis vs. a frequentist analysis
Asking about Type 1 and power (i.e. one minus the probability of Type 2 error) impli |
13,406 | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research? | Bayesian statistics can be derived from a few Logical principles. Try Searching "probability as extended logic" and you will find more in depth analysis of the fundamentals. But basically, Bayesian statistics rests on three basic "desiderata" or normative principles:
The plausability of a proposition is to be repres... | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavi | Bayesian statistics can be derived from a few Logical principles. Try Searching "probability as extended logic" and you will find more in depth analysis of the fundamentals. But basically, Bayesian | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research?
Bayesian statistics can be derived from a few Logical principles. Try Searching "probability as extended logic" and you will find more in depth analysis of the fundamentals. But basically, Bayesian stati... | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavi
Bayesian statistics can be derived from a few Logical principles. Try Searching "probability as extended logic" and you will find more in depth analysis of the fundamentals. But basically, Bayesian |
13,407 | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research? | I am not familiar with Bayesian Statistics myself but I do know that Skeptics Guide to the Universe Episode 294 has and interview with Eric-Jan Wagenmakers where they discuss Bayesian Statistics. Here is a link to the podcast:
http://www.theskepticsguide.org/archive/podcastinfo.aspx?mid=1&pid=294 | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavi | I am not familiar with Bayesian Statistics myself but I do know that Skeptics Guide to the Universe Episode 294 has and interview with Eric-Jan Wagenmakers where they discuss Bayesian Statistics. Here | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research?
I am not familiar with Bayesian Statistics myself but I do know that Skeptics Guide to the Universe Episode 294 has and interview with Eric-Jan Wagenmakers where they discuss Bayesian Statistics. Here is a... | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavi
I am not familiar with Bayesian Statistics myself but I do know that Skeptics Guide to the Universe Episode 294 has and interview with Eric-Jan Wagenmakers where they discuss Bayesian Statistics. Here |
13,408 | Is my weatherman accurate? | In effect you are thinking of a model in which the true chance of rain, p, is a function of the predicted chance q: p = p(q). Each time a prediction is made, you observe one realization of a Bernoulli variate having probability p(q) of success. This is a classic logistic regression setup if you are willing to model t... | Is my weatherman accurate? | In effect you are thinking of a model in which the true chance of rain, p, is a function of the predicted chance q: p = p(q). Each time a prediction is made, you observe one realization of a Bernoull | Is my weatherman accurate?
In effect you are thinking of a model in which the true chance of rain, p, is a function of the predicted chance q: p = p(q). Each time a prediction is made, you observe one realization of a Bernoulli variate having probability p(q) of success. This is a classic logistic regression setup if... | Is my weatherman accurate?
In effect you are thinking of a model in which the true chance of rain, p, is a function of the predicted chance q: p = p(q). Each time a prediction is made, you observe one realization of a Bernoull |
13,409 | Is my weatherman accurate? | Comparison of probability forecast for binary event (or discrete Random Variable) can be done upon the Brier score
but you can also use ROC curve since any probability forecast of this type can be transformed into a dicrimination procedure with a varying threshold
Indeed you can say "it will rain" if your probabilit... | Is my weatherman accurate? | Comparison of probability forecast for binary event (or discrete Random Variable) can be done upon the Brier score
but you can also use ROC curve since any probability forecast of this type can be t | Is my weatherman accurate?
Comparison of probability forecast for binary event (or discrete Random Variable) can be done upon the Brier score
but you can also use ROC curve since any probability forecast of this type can be transformed into a dicrimination procedure with a varying threshold
Indeed you can say "it wi... | Is my weatherman accurate?
Comparison of probability forecast for binary event (or discrete Random Variable) can be done upon the Brier score
but you can also use ROC curve since any probability forecast of this type can be t |
13,410 | Is my weatherman accurate? | When the forecast says "X percent chance of rain in (area)", it means that the numerical weather model has indicated rain in X percent of the area, for the time interval in question. For example, it would normally be accurate to predict "100 percent chance of rain in North America". Bear in mind that the models are g... | Is my weatherman accurate? | When the forecast says "X percent chance of rain in (area)", it means that the numerical weather model has indicated rain in X percent of the area, for the time interval in question. For example, it | Is my weatherman accurate?
When the forecast says "X percent chance of rain in (area)", it means that the numerical weather model has indicated rain in X percent of the area, for the time interval in question. For example, it would normally be accurate to predict "100 percent chance of rain in North America". Bear in... | Is my weatherman accurate?
When the forecast says "X percent chance of rain in (area)", it means that the numerical weather model has indicated rain in X percent of the area, for the time interval in question. For example, it |
13,411 | Is my weatherman accurate? | The Brier Score approach is very simple and the most directly applicable way verify accuracy of a predicted outcome versus binary event.
Don't rely on just formulas ...plot the scores for different periods of time, data, errors, [weighted] rolling average of data, errors ... it's tough to say what visual analysis mig... | Is my weatherman accurate? | The Brier Score approach is very simple and the most directly applicable way verify accuracy of a predicted outcome versus binary event.
Don't rely on just formulas ...plot the scores for different | Is my weatherman accurate?
The Brier Score approach is very simple and the most directly applicable way verify accuracy of a predicted outcome versus binary event.
Don't rely on just formulas ...plot the scores for different periods of time, data, errors, [weighted] rolling average of data, errors ... it's tough to s... | Is my weatherman accurate?
The Brier Score approach is very simple and the most directly applicable way verify accuracy of a predicted outcome versus binary event.
Don't rely on just formulas ...plot the scores for different |
13,412 | Is my weatherman accurate? | How about just binning the given predictions and taking the observed fractions as your estimate for each bin?
You can generalise this to a continuous model by weighing all the observations around your value of interest (say the prediction by tomorrow) by a Gaussian and seeing what the weighted average is.
You can guess... | Is my weatherman accurate? | How about just binning the given predictions and taking the observed fractions as your estimate for each bin?
You can generalise this to a continuous model by weighing all the observations around your | Is my weatherman accurate?
How about just binning the given predictions and taking the observed fractions as your estimate for each bin?
You can generalise this to a continuous model by weighing all the observations around your value of interest (say the prediction by tomorrow) by a Gaussian and seeing what the weighte... | Is my weatherman accurate?
How about just binning the given predictions and taking the observed fractions as your estimate for each bin?
You can generalise this to a continuous model by weighing all the observations around your |
13,413 | Is my weatherman accurate? | Do you want to know if his forecast is more accurate than another forecast? If so, you can look at basic accuracy metrics for probabilistic classification like cross-entropy, precision/recall, ROC curves, and the f1-score.
Determining if the forecast is objectively good is a different matter. One option is to look ... | Is my weatherman accurate? | Do you want to know if his forecast is more accurate than another forecast? If so, you can look at basic accuracy metrics for probabilistic classification like cross-entropy, precision/recall, ROC cu | Is my weatherman accurate?
Do you want to know if his forecast is more accurate than another forecast? If so, you can look at basic accuracy metrics for probabilistic classification like cross-entropy, precision/recall, ROC curves, and the f1-score.
Determining if the forecast is objectively good is a different matt... | Is my weatherman accurate?
Do you want to know if his forecast is more accurate than another forecast? If so, you can look at basic accuracy metrics for probabilistic classification like cross-entropy, precision/recall, ROC cu |
13,414 | RMSE vs Standard deviation in population | TLDR; While the formulas may be similar, RMSE and standard deviation have different usage.
You are right that both standard deviation and RMSE are similar because they are square roots of squared differences between some values. Nonetheless, they are not the same. Standard deviation is used to measure the spread of dat... | RMSE vs Standard deviation in population | TLDR; While the formulas may be similar, RMSE and standard deviation have different usage.
You are right that both standard deviation and RMSE are similar because they are square roots of squared diff | RMSE vs Standard deviation in population
TLDR; While the formulas may be similar, RMSE and standard deviation have different usage.
You are right that both standard deviation and RMSE are similar because they are square roots of squared differences between some values. Nonetheless, they are not the same. Standard devia... | RMSE vs Standard deviation in population
TLDR; While the formulas may be similar, RMSE and standard deviation have different usage.
You are right that both standard deviation and RMSE are similar because they are square roots of squared diff |
13,415 | RMSE vs Standard deviation in population | This will make bit clear,
RMSE calculated between two sets, eg: set and predicted set, to calculate the error,
eg :
price Vs predicted price
10 12
12 10
13 17
$$ {RMSE}=\sqrt{\frac{\sum_{i=1}^N{(F_i - O_i)^2}}{N}} $$
f = forecasts (expected values or unknown results),
o = observed values (know... | RMSE vs Standard deviation in population | This will make bit clear,
RMSE calculated between two sets, eg: set and predicted set, to calculate the error,
eg :
price Vs predicted price
10 12
12 10
13 17
$$ {RMSE}=\sqrt | RMSE vs Standard deviation in population
This will make bit clear,
RMSE calculated between two sets, eg: set and predicted set, to calculate the error,
eg :
price Vs predicted price
10 12
12 10
13 17
$$ {RMSE}=\sqrt{\frac{\sum_{i=1}^N{(F_i - O_i)^2}}{N}} $$
f = forecasts (expected values or un... | RMSE vs Standard deviation in population
This will make bit clear,
RMSE calculated between two sets, eg: set and predicted set, to calculate the error,
eg :
price Vs predicted price
10 12
12 10
13 17
$$ {RMSE}=\sqrt |
13,416 | Interpretation of .L & .Q output from a negative binomial GLM with categorical data | Your variables aren't just coded as factors (to make them categorical), they are coded as ordered factors. Then, by default, R fits a series of polynomial functions to the levels of the variable. The first is linear (.L), the second is quadratic (.Q), the third (if you had enough levels) would be cubic, etc. R will ... | Interpretation of .L & .Q output from a negative binomial GLM with categorical data | Your variables aren't just coded as factors (to make them categorical), they are coded as ordered factors. Then, by default, R fits a series of polynomial functions to the levels of the variable. Th | Interpretation of .L & .Q output from a negative binomial GLM with categorical data
Your variables aren't just coded as factors (to make them categorical), they are coded as ordered factors. Then, by default, R fits a series of polynomial functions to the levels of the variable. The first is linear (.L), the second i... | Interpretation of .L & .Q output from a negative binomial GLM with categorical data
Your variables aren't just coded as factors (to make them categorical), they are coded as ordered factors. Then, by default, R fits a series of polynomial functions to the levels of the variable. Th |
13,417 | Interpretation of LASSO regression coefficients | Are the LASSO coefficients interpreted in the same method as logistic regression?
Let me rephrase: Are the LASSO coefficients interpreted in the same way as, for example, OLS maximum likelihood coefficients in a logistic regression?
LASSO (a penalized estimation method) aims at estimating the same quantities (model co... | Interpretation of LASSO regression coefficients | Are the LASSO coefficients interpreted in the same method as logistic regression?
Let me rephrase: Are the LASSO coefficients interpreted in the same way as, for example, OLS maximum likelihood coeff | Interpretation of LASSO regression coefficients
Are the LASSO coefficients interpreted in the same method as logistic regression?
Let me rephrase: Are the LASSO coefficients interpreted in the same way as, for example, OLS maximum likelihood coefficients in a logistic regression?
LASSO (a penalized estimation method) ... | Interpretation of LASSO regression coefficients
Are the LASSO coefficients interpreted in the same method as logistic regression?
Let me rephrase: Are the LASSO coefficients interpreted in the same way as, for example, OLS maximum likelihood coeff |
13,418 | Making sense of independent component analysis | Here's my attempt.
Background
Consider the following two cases.
You are a private eye at a party. Suddenly, you see one of your old clients talking to someone, and you can hear some of the words but not quite, because you also hear someone else who's next to him, participating in an unrelated discussion about sports. ... | Making sense of independent component analysis | Here's my attempt.
Background
Consider the following two cases.
You are a private eye at a party. Suddenly, you see one of your old clients talking to someone, and you can hear some of the words but | Making sense of independent component analysis
Here's my attempt.
Background
Consider the following two cases.
You are a private eye at a party. Suddenly, you see one of your old clients talking to someone, and you can hear some of the words but not quite, because you also hear someone else who's next to him, particip... | Making sense of independent component analysis
Here's my attempt.
Background
Consider the following two cases.
You are a private eye at a party. Suddenly, you see one of your old clients talking to someone, and you can hear some of the words but |
13,419 | Making sense of independent component analysis | Very simple. Imagine you, your grandma and the family members are gathered around the table. Larger groups of people tend to break up where the chat topic is specific to that subgroup. Your grandma sits there and hears the noise of all of people speaking, what appears to be just a cacophony. If she turns to one group, ... | Making sense of independent component analysis | Very simple. Imagine you, your grandma and the family members are gathered around the table. Larger groups of people tend to break up where the chat topic is specific to that subgroup. Your grandma si | Making sense of independent component analysis
Very simple. Imagine you, your grandma and the family members are gathered around the table. Larger groups of people tend to break up where the chat topic is specific to that subgroup. Your grandma sits there and hears the noise of all of people speaking, what appears to b... | Making sense of independent component analysis
Very simple. Imagine you, your grandma and the family members are gathered around the table. Larger groups of people tend to break up where the chat topic is specific to that subgroup. Your grandma si |
13,420 | Sampling from von Mises-Fisher distribution in Python? | Finally, I got it. Here is my answer.
I finally put my hands on Directional Statistics (Mardia and Jupp, 1999) and on the Ulrich-Wood's algorithm for sampling. I post here what I understood from it, i.e. my code (in Python).
The rejection sampling scheme:
def rW(n, kappa, m):
dim = m-1
b = dim / (np.sqrt(4*kapp... | Sampling from von Mises-Fisher distribution in Python? | Finally, I got it. Here is my answer.
I finally put my hands on Directional Statistics (Mardia and Jupp, 1999) and on the Ulrich-Wood's algorithm for sampling. I post here what I understood from it, i | Sampling from von Mises-Fisher distribution in Python?
Finally, I got it. Here is my answer.
I finally put my hands on Directional Statistics (Mardia and Jupp, 1999) and on the Ulrich-Wood's algorithm for sampling. I post here what I understood from it, i.e. my code (in Python).
The rejection sampling scheme:
def rW(n,... | Sampling from von Mises-Fisher distribution in Python?
Finally, I got it. Here is my answer.
I finally put my hands on Directional Statistics (Mardia and Jupp, 1999) and on the Ulrich-Wood's algorithm for sampling. I post here what I understood from it, i |
13,421 | Sampling from von Mises-Fisher distribution in Python? | (I apologize for the formatting here, I created an account just to reply to this question, since I was also trying to figure this out recently).
The answer of mic isn't quite right, the vector $v$ needs to come from $S^{p-2}$ in the tangent space to $\mu$, that is, $v$ should be a unit vector orthogonal to $\mu$. Other... | Sampling from von Mises-Fisher distribution in Python? | (I apologize for the formatting here, I created an account just to reply to this question, since I was also trying to figure this out recently).
The answer of mic isn't quite right, the vector $v$ nee | Sampling from von Mises-Fisher distribution in Python?
(I apologize for the formatting here, I created an account just to reply to this question, since I was also trying to figure this out recently).
The answer of mic isn't quite right, the vector $v$ needs to come from $S^{p-2}$ in the tangent space to $\mu$, that is,... | Sampling from von Mises-Fisher distribution in Python?
(I apologize for the formatting here, I created an account just to reply to this question, since I was also trying to figure this out recently).
The answer of mic isn't quite right, the vector $v$ nee |
13,422 | Why $\sqrt{n}$ in the definition of asymptotic normality? | We don't get to choose here. The "normalizing" factor, in essence is a "variance-stabilizing to something finite" factor, so as for the expression not to go to zero or to infinity as sample size goes to infinity, but to maintain a distribution at the limit.
So it has to be whatever it has to be in each case. Of course ... | Why $\sqrt{n}$ in the definition of asymptotic normality? | We don't get to choose here. The "normalizing" factor, in essence is a "variance-stabilizing to something finite" factor, so as for the expression not to go to zero or to infinity as sample size goes | Why $\sqrt{n}$ in the definition of asymptotic normality?
We don't get to choose here. The "normalizing" factor, in essence is a "variance-stabilizing to something finite" factor, so as for the expression not to go to zero or to infinity as sample size goes to infinity, but to maintain a distribution at the limit.
So i... | Why $\sqrt{n}$ in the definition of asymptotic normality?
We don't get to choose here. The "normalizing" factor, in essence is a "variance-stabilizing to something finite" factor, so as for the expression not to go to zero or to infinity as sample size goes |
13,423 | Why $\sqrt{n}$ in the definition of asymptotic normality? | You were on the right track with a sample mean variance intuition. Re-arrange the condition:
$$\sqrt{n}(U_n - \theta) \to N(0,v)$$
$$(U_n - \theta) \to \frac{N(0,v)}{\sqrt{n}}$$
$$U_n \to N(\theta,\frac{v}{n})$$
The last equation is informal. However, it's in some way more intuitive: you say that the deviation of $U_... | Why $\sqrt{n}$ in the definition of asymptotic normality? | You were on the right track with a sample mean variance intuition. Re-arrange the condition:
$$\sqrt{n}(U_n - \theta) \to N(0,v)$$
$$(U_n - \theta) \to \frac{N(0,v)}{\sqrt{n}}$$
$$U_n \to N(\theta,\f | Why $\sqrt{n}$ in the definition of asymptotic normality?
You were on the right track with a sample mean variance intuition. Re-arrange the condition:
$$\sqrt{n}(U_n - \theta) \to N(0,v)$$
$$(U_n - \theta) \to \frac{N(0,v)}{\sqrt{n}}$$
$$U_n \to N(\theta,\frac{v}{n})$$
The last equation is informal. However, it's in s... | Why $\sqrt{n}$ in the definition of asymptotic normality?
You were on the right track with a sample mean variance intuition. Re-arrange the condition:
$$\sqrt{n}(U_n - \theta) \to N(0,v)$$
$$(U_n - \theta) \to \frac{N(0,v)}{\sqrt{n}}$$
$$U_n \to N(\theta,\f |
13,424 | stochastic vs deterministic trend/seasonality in time series forecasting | 1) As regards your first question, some tests statistics have been developed and discussed in the literature to test the null of stationarity and the null of a unit root. Some of the many papers that were written on this issue are the following:
Related to the trend:
Dickey, D. y Fuller, W. (1979a), Distribution of th... | stochastic vs deterministic trend/seasonality in time series forecasting | 1) As regards your first question, some tests statistics have been developed and discussed in the literature to test the null of stationarity and the null of a unit root. Some of the many papers that | stochastic vs deterministic trend/seasonality in time series forecasting
1) As regards your first question, some tests statistics have been developed and discussed in the literature to test the null of stationarity and the null of a unit root. Some of the many papers that were written on this issue are the following:
R... | stochastic vs deterministic trend/seasonality in time series forecasting
1) As regards your first question, some tests statistics have been developed and discussed in the literature to test the null of stationarity and the null of a unit root. Some of the many papers that |
13,425 | stochastic vs deterministic trend/seasonality in time series forecasting | With respect to your non-seasonal data ...Trends can be of two forms
y(t)=y(t−1)+θ0 (A) Stochastic Trend
or
Y(t)=a+bx1+cx2 (B) Deterministic Trend
etc where x1=1,2,3,4....t and x2=0,0,0,0,0,1,2,3,4 thus one trend applies to observations 1−t and a second trend applies to observations 6 to t.
Your non-seas... | stochastic vs deterministic trend/seasonality in time series forecasting | With respect to your non-seasonal data ...Trends can be of two forms
y(t)=y(t−1)+θ0 (A) Stochastic Trend
or
Y(t)=a+bx1+cx2 (B) Deterministic Trend
etc where x1=1,2,3,4....t and x2=0,0,0 | stochastic vs deterministic trend/seasonality in time series forecasting
With respect to your non-seasonal data ...Trends can be of two forms
y(t)=y(t−1)+θ0 (A) Stochastic Trend
or
Y(t)=a+bx1+cx2 (B) Deterministic Trend
etc where x1=1,2,3,4....t and x2=0,0,0,0,0,1,2,3,4 thus one trend applies to observat... | stochastic vs deterministic trend/seasonality in time series forecasting
With respect to your non-seasonal data ...Trends can be of two forms
y(t)=y(t−1)+θ0 (A) Stochastic Trend
or
Y(t)=a+bx1+cx2 (B) Deterministic Trend
etc where x1=1,2,3,4....t and x2=0,0,0 |
13,426 | What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference? | I'm not an expert in any of these, but I thought I'd put them out there anyway to see what the community thought. Corrections are welcome.
One increasingly popular method, which is not terribly straightforward to implement, is called Hamiltonian Monte Carlo (or sometimes Hybrid Monte Carlo). It uses a physical model ... | What are some well known improvements over textbook MCMC algorithms that people use for bayesian inf | I'm not an expert in any of these, but I thought I'd put them out there anyway to see what the community thought. Corrections are welcome.
One increasingly popular method, which is not terribly strai | What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference?
I'm not an expert in any of these, but I thought I'd put them out there anyway to see what the community thought. Corrections are welcome.
One increasingly popular method, which is not terribly straightforward t... | What are some well known improvements over textbook MCMC algorithms that people use for bayesian inf
I'm not an expert in any of these, but I thought I'd put them out there anyway to see what the community thought. Corrections are welcome.
One increasingly popular method, which is not terribly strai |
13,427 | Dealing with ties, weights and voting in kNN | When doing kNN you need to keep one thing in mind, namely that it's not a strictly, mathematically derived algorithm, but rather a simple classifier / regressor based on one intuition - the underlying function doesn't change much when the arguments don't change much. Or in other words the underlying function is locally... | Dealing with ties, weights and voting in kNN | When doing kNN you need to keep one thing in mind, namely that it's not a strictly, mathematically derived algorithm, but rather a simple classifier / regressor based on one intuition - the underlying | Dealing with ties, weights and voting in kNN
When doing kNN you need to keep one thing in mind, namely that it's not a strictly, mathematically derived algorithm, but rather a simple classifier / regressor based on one intuition - the underlying function doesn't change much when the arguments don't change much. Or in o... | Dealing with ties, weights and voting in kNN
When doing kNN you need to keep one thing in mind, namely that it's not a strictly, mathematically derived algorithm, but rather a simple classifier / regressor based on one intuition - the underlying |
13,428 | Dealing with ties, weights and voting in kNN | The ideal way to break a tie for a k nearest neighbor in my view would be to decrease k by 1 until you have broken the tie. This will always work regardless of the vote weighting scheme, since a tie is impossible when k = 1. If you were to increase k, pending your weighting scheme and number of categories, you would no... | Dealing with ties, weights and voting in kNN | The ideal way to break a tie for a k nearest neighbor in my view would be to decrease k by 1 until you have broken the tie. This will always work regardless of the vote weighting scheme, since a tie i | Dealing with ties, weights and voting in kNN
The ideal way to break a tie for a k nearest neighbor in my view would be to decrease k by 1 until you have broken the tie. This will always work regardless of the vote weighting scheme, since a tie is impossible when k = 1. If you were to increase k, pending your weighting ... | Dealing with ties, weights and voting in kNN
The ideal way to break a tie for a k nearest neighbor in my view would be to decrease k by 1 until you have broken the tie. This will always work regardless of the vote weighting scheme, since a tie i |
13,429 | Dealing with ties, weights and voting in kNN | About this tie part, the best baseline idea for ties is usually random breaking, so selecting random class of all winning the voting and randomly selecting a subset of tied objects large enough to fill k.
Such a solution stresses the fact that those are pathological cases that simply don't provide enough informatio... | Dealing with ties, weights and voting in kNN | About this tie part, the best baseline idea for ties is usually random breaking, so selecting random class of all winning the voting and randomly selecting a subset of tied objects large enough to fil | Dealing with ties, weights and voting in kNN
About this tie part, the best baseline idea for ties is usually random breaking, so selecting random class of all winning the voting and randomly selecting a subset of tied objects large enough to fill k.
Such a solution stresses the fact that those are pathological case... | Dealing with ties, weights and voting in kNN
About this tie part, the best baseline idea for ties is usually random breaking, so selecting random class of all winning the voting and randomly selecting a subset of tied objects large enough to fil |
13,430 | Dealing with ties, weights and voting in kNN | One possible way is to have the algorithm automatically increase or decrease k until you get a clear winner. | Dealing with ties, weights and voting in kNN | One possible way is to have the algorithm automatically increase or decrease k until you get a clear winner. | Dealing with ties, weights and voting in kNN
One possible way is to have the algorithm automatically increase or decrease k until you get a clear winner. | Dealing with ties, weights and voting in kNN
One possible way is to have the algorithm automatically increase or decrease k until you get a clear winner. |
13,431 | How to do a generalized linear model with multiple dependent variables in R? | The short answer is that glm doesn't work like that. The lm will create mlm objects if you give it a matrix, but this is not widely supported in the generics and anyway couldn't easily generalize to glm because users need to be able to specify dual column dependent variables for logistic regression models.
The solutio... | How to do a generalized linear model with multiple dependent variables in R? | The short answer is that glm doesn't work like that. The lm will create mlm objects if you give it a matrix, but this is not widely supported in the generics and anyway couldn't easily generalize to | How to do a generalized linear model with multiple dependent variables in R?
The short answer is that glm doesn't work like that. The lm will create mlm objects if you give it a matrix, but this is not widely supported in the generics and anyway couldn't easily generalize to glm because users need to be able to specif... | How to do a generalized linear model with multiple dependent variables in R?
The short answer is that glm doesn't work like that. The lm will create mlm objects if you give it a matrix, but this is not widely supported in the generics and anyway couldn't easily generalize to |
13,432 | How to do a generalized linear model with multiple dependent variables in R? | I was told Multivariate Generalized Linear (Mixed) Models exists that address your problem. I'm not an expert about it, but I would have a look to SABRE documentation and this book on multivariate GLMs. Maybe they help... | How to do a generalized linear model with multiple dependent variables in R? | I was told Multivariate Generalized Linear (Mixed) Models exists that address your problem. I'm not an expert about it, but I would have a look to SABRE documentation and this book on multivariate GLM | How to do a generalized linear model with multiple dependent variables in R?
I was told Multivariate Generalized Linear (Mixed) Models exists that address your problem. I'm not an expert about it, but I would have a look to SABRE documentation and this book on multivariate GLMs. Maybe they help... | How to do a generalized linear model with multiple dependent variables in R?
I was told Multivariate Generalized Linear (Mixed) Models exists that address your problem. I'm not an expert about it, but I would have a look to SABRE documentation and this book on multivariate GLM |
13,433 | Examples of hidden Markov models problems? | I've used HMM in a demand / inventory level estimation scenario, where we had goods being purchased from many stores that might or might not be out of inventory of the goods. The sequence of daily demands for these items thus contained zeroes that were legitimate zero demand days and also zeroes that were because the ... | Examples of hidden Markov models problems? | I've used HMM in a demand / inventory level estimation scenario, where we had goods being purchased from many stores that might or might not be out of inventory of the goods. The sequence of daily de | Examples of hidden Markov models problems?
I've used HMM in a demand / inventory level estimation scenario, where we had goods being purchased from many stores that might or might not be out of inventory of the goods. The sequence of daily demands for these items thus contained zeroes that were legitimate zero demand ... | Examples of hidden Markov models problems?
I've used HMM in a demand / inventory level estimation scenario, where we had goods being purchased from many stores that might or might not be out of inventory of the goods. The sequence of daily de |
13,434 | Examples of hidden Markov models problems? | I pretty much experienced the same thing and didn't find much beyond the weather. Areas that come to mind include: speech recognition, change point detection, tagging parts of speech in text, aligning overlapping items/text, and recognizing sign language.
One example I found and did some exploration of was in Section 8... | Examples of hidden Markov models problems? | I pretty much experienced the same thing and didn't find much beyond the weather. Areas that come to mind include: speech recognition, change point detection, tagging parts of speech in text, aligning | Examples of hidden Markov models problems?
I pretty much experienced the same thing and didn't find much beyond the weather. Areas that come to mind include: speech recognition, change point detection, tagging parts of speech in text, aligning overlapping items/text, and recognizing sign language.
One example I found a... | Examples of hidden Markov models problems?
I pretty much experienced the same thing and didn't find much beyond the weather. Areas that come to mind include: speech recognition, change point detection, tagging parts of speech in text, aligning |
13,435 | Examples of hidden Markov models problems? | Most speech recognition software uses Hidden Markov Models. You can experiment with natural language processing if you want to get a feel for HMM applications.
Here's a good source: Probabilistic Graphical Models, by Koller and Friedman. | Examples of hidden Markov models problems? | Most speech recognition software uses Hidden Markov Models. You can experiment with natural language processing if you want to get a feel for HMM applications.
Here's a good source: Probabilistic Grap | Examples of hidden Markov models problems?
Most speech recognition software uses Hidden Markov Models. You can experiment with natural language processing if you want to get a feel for HMM applications.
Here's a good source: Probabilistic Graphical Models, by Koller and Friedman. | Examples of hidden Markov models problems?
Most speech recognition software uses Hidden Markov Models. You can experiment with natural language processing if you want to get a feel for HMM applications.
Here's a good source: Probabilistic Grap |
13,436 | Examples of hidden Markov models problems? | Hidden markov models are very useful in monitoring HIV. HIV enters the blood stream and looks for the immune response cells. It then sits on the protein content of the cell and gets into the core of the cell and changes the DNA content of the cell and starts proliferation of virions until it burst out of the cells. ... | Examples of hidden Markov models problems? | Hidden markov models are very useful in monitoring HIV. HIV enters the blood stream and looks for the immune response cells. It then sits on the protein content of the cell and gets into the core of | Examples of hidden Markov models problems?
Hidden markov models are very useful in monitoring HIV. HIV enters the blood stream and looks for the immune response cells. It then sits on the protein content of the cell and gets into the core of the cell and changes the DNA content of the cell and starts proliferation of... | Examples of hidden Markov models problems?
Hidden markov models are very useful in monitoring HIV. HIV enters the blood stream and looks for the immune response cells. It then sits on the protein content of the cell and gets into the core of |
13,437 | Examples of hidden Markov models problems? | For me, very nice application of HMM is chord identification in musical composition. See for example this lecture. | Examples of hidden Markov models problems? | For me, very nice application of HMM is chord identification in musical composition. See for example this lecture. | Examples of hidden Markov models problems?
For me, very nice application of HMM is chord identification in musical composition. See for example this lecture. | Examples of hidden Markov models problems?
For me, very nice application of HMM is chord identification in musical composition. See for example this lecture. |
13,438 | Examples of hidden Markov models problems? | Markov models may be useful in analyzing the interactions of a user with a website - For Example on Amazon.com where figuring out what series of interactions lead to a checkout to give recommendations in the future.
A fun example showing the use of Markov Model is the following-
http://freakonometrics.blog.free.fr/inde... | Examples of hidden Markov models problems? | Markov models may be useful in analyzing the interactions of a user with a website - For Example on Amazon.com where figuring out what series of interactions lead to a checkout to give recommendations | Examples of hidden Markov models problems?
Markov models may be useful in analyzing the interactions of a user with a website - For Example on Amazon.com where figuring out what series of interactions lead to a checkout to give recommendations in the future.
A fun example showing the use of Markov Model is the followin... | Examples of hidden Markov models problems?
Markov models may be useful in analyzing the interactions of a user with a website - For Example on Amazon.com where figuring out what series of interactions lead to a checkout to give recommendations |
13,439 | Proportion of explained variance in a mixed-effects model | I can provide some references:
Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22, 3527-3541. DOI:10.1002/sim.1572
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., & Schabenberger, O. (2008). An $R^2$ statistic for fixed effects in the linear mixed mod... | Proportion of explained variance in a mixed-effects model | I can provide some references:
Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22, 3527-3541. DOI:10.1002/sim.1572
Edwards, L. J., Muller, K. E., W | Proportion of explained variance in a mixed-effects model
I can provide some references:
Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22, 3527-3541. DOI:10.1002/sim.1572
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., & Schabenberger, O. (2008). An... | Proportion of explained variance in a mixed-effects model
I can provide some references:
Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22, 3527-3541. DOI:10.1002/sim.1572
Edwards, L. J., Muller, K. E., W |
13,440 | Proportion of explained variance in a mixed-effects model | According to this blog post from 2013, the MuMIn package in R can provide R$^2$ values for mixed models ala an approach developed by Nakagawa & Schielzeth 2013$^1$ (which was mentioned in a previous answer).
#load packages
library(lme4)
library(MuMIn)
#Fit Model
m <- lmer(mpg ~ gear + disp + (1|cyl), data = mtcars)
#... | Proportion of explained variance in a mixed-effects model | According to this blog post from 2013, the MuMIn package in R can provide R$^2$ values for mixed models ala an approach developed by Nakagawa & Schielzeth 2013$^1$ (which was mentioned in a previous a | Proportion of explained variance in a mixed-effects model
According to this blog post from 2013, the MuMIn package in R can provide R$^2$ values for mixed models ala an approach developed by Nakagawa & Schielzeth 2013$^1$ (which was mentioned in a previous answer).
#load packages
library(lme4)
library(MuMIn)
#Fit Mode... | Proportion of explained variance in a mixed-effects model
According to this blog post from 2013, the MuMIn package in R can provide R$^2$ values for mixed models ala an approach developed by Nakagawa & Schielzeth 2013$^1$ (which was mentioned in a previous a |
13,441 | What's the history of box plots, and how did the "box and whiskers" design evolve? | Chief Executive Officer summary
The history is much longer and more complicated than many people think it is.
Executive summary
The history of what Tukey called box plots is tangled up with that of what are now often called dot or strip plots (dozens of other names) and with representations of the empirical quantile ... | What's the history of box plots, and how did the "box and whiskers" design evolve? | Chief Executive Officer summary
The history is much longer and more complicated than many people think it is.
Executive summary
The history of what Tukey called box plots is tangled up with that of | What's the history of box plots, and how did the "box and whiskers" design evolve?
Chief Executive Officer summary
The history is much longer and more complicated than many people think it is.
Executive summary
The history of what Tukey called box plots is tangled up with that of what are now often called dot or stri... | What's the history of box plots, and how did the "box and whiskers" design evolve?
Chief Executive Officer summary
The history is much longer and more complicated than many people think it is.
Executive summary
The history of what Tukey called box plots is tangled up with that of |
13,442 | Evaluate Random Forest: OOB vs CV | Note: While I feel that my answer is probably correct, I also feel doubtful due to the fact that I made all this up by thinking about this problem only after reading this question for about 30-60 minutes. So you better be sceptical and scrutinize this and not get fooled by my possibly overly confident writing style (me... | Evaluate Random Forest: OOB vs CV | Note: While I feel that my answer is probably correct, I also feel doubtful due to the fact that I made all this up by thinking about this problem only after reading this question for about 30-60 minu | Evaluate Random Forest: OOB vs CV
Note: While I feel that my answer is probably correct, I also feel doubtful due to the fact that I made all this up by thinking about this problem only after reading this question for about 30-60 minutes. So you better be sceptical and scrutinize this and not get fooled by my possibly ... | Evaluate Random Forest: OOB vs CV
Note: While I feel that my answer is probably correct, I also feel doubtful due to the fact that I made all this up by thinking about this problem only after reading this question for about 30-60 minu |
13,443 | Evaluate Random Forest: OOB vs CV | The motivation :
Therefore in my view the only reason why OOBE is a pessimistic
estimation of forest's error is only because it usually trains by a
smaller number of samples than usually done with k-fold
cross-validation (where 10 folds is common).
does not seem correct.
It is not true that OOBE error estimate being ... | Evaluate Random Forest: OOB vs CV | The motivation :
Therefore in my view the only reason why OOBE is a pessimistic
estimation of forest's error is only because it usually trains by a
smaller number of samples than usually done with k- | Evaluate Random Forest: OOB vs CV
The motivation :
Therefore in my view the only reason why OOBE is a pessimistic
estimation of forest's error is only because it usually trains by a
smaller number of samples than usually done with k-fold
cross-validation (where 10 folds is common).
does not seem correct.
It is not tr... | Evaluate Random Forest: OOB vs CV
The motivation :
Therefore in my view the only reason why OOBE is a pessimistic
estimation of forest's error is only because it usually trains by a
smaller number of samples than usually done with k- |
13,444 | Are Random Forest and Boosting parametric or non-parametric? | Parametrical models have parameters (infering them)or assumptions regarding the data distribution, whereas RF ,neural nets or boosting trees have parameters related with the algorithm itself, but they don't need assumptions about your data distribution or classify your data into a theoretical distribution. In fact almo... | Are Random Forest and Boosting parametric or non-parametric? | Parametrical models have parameters (infering them)or assumptions regarding the data distribution, whereas RF ,neural nets or boosting trees have parameters related with the algorithm itself, but they | Are Random Forest and Boosting parametric or non-parametric?
Parametrical models have parameters (infering them)or assumptions regarding the data distribution, whereas RF ,neural nets or boosting trees have parameters related with the algorithm itself, but they don't need assumptions about your data distribution or cla... | Are Random Forest and Boosting parametric or non-parametric?
Parametrical models have parameters (infering them)or assumptions regarding the data distribution, whereas RF ,neural nets or boosting trees have parameters related with the algorithm itself, but they |
13,445 | Are Random Forest and Boosting parametric or non-parametric? | I think the criterion for parametric and non-parametric is this: whether the number of parameters grows with the number of training samples. For logistic regression and svm, when you select the features, you won't get more parameters by adding more training data. But for RF and so on, the details of model will change (... | Are Random Forest and Boosting parametric or non-parametric? | I think the criterion for parametric and non-parametric is this: whether the number of parameters grows with the number of training samples. For logistic regression and svm, when you select the featur | Are Random Forest and Boosting parametric or non-parametric?
I think the criterion for parametric and non-parametric is this: whether the number of parameters grows with the number of training samples. For logistic regression and svm, when you select the features, you won't get more parameters by adding more training d... | Are Random Forest and Boosting parametric or non-parametric?
I think the criterion for parametric and non-parametric is this: whether the number of parameters grows with the number of training samples. For logistic regression and svm, when you select the featur |
13,446 | Are Random Forest and Boosting parametric or non-parametric? | The term "non-parametric" is a bit of a misnomer, as generally these models/algorithms are defined as having the number of parameters which increase as the sample size increases. Whether a RF does this or not depends on how the tree splitting/pruning algorithm works. If no pruning is done, and splitting it based on sam... | Are Random Forest and Boosting parametric or non-parametric? | The term "non-parametric" is a bit of a misnomer, as generally these models/algorithms are defined as having the number of parameters which increase as the sample size increases. Whether a RF does thi | Are Random Forest and Boosting parametric or non-parametric?
The term "non-parametric" is a bit of a misnomer, as generally these models/algorithms are defined as having the number of parameters which increase as the sample size increases. Whether a RF does this or not depends on how the tree splitting/pruning algorith... | Are Random Forest and Boosting parametric or non-parametric?
The term "non-parametric" is a bit of a misnomer, as generally these models/algorithms are defined as having the number of parameters which increase as the sample size increases. Whether a RF does thi |
13,447 | Are Random Forest and Boosting parametric or non-parametric? | In statistical sense, the model is parametric, if parameters are learned or inferred based on the data. A tree in this sense is nonparametric. Of course the tree depth is a parameter of the algorithm, but it is not inherently derived from the data, but rather an input parameter that has to be provided by the user. | Are Random Forest and Boosting parametric or non-parametric? | In statistical sense, the model is parametric, if parameters are learned or inferred based on the data. A tree in this sense is nonparametric. Of course the tree depth is a parameter of the algorithm, | Are Random Forest and Boosting parametric or non-parametric?
In statistical sense, the model is parametric, if parameters are learned or inferred based on the data. A tree in this sense is nonparametric. Of course the tree depth is a parameter of the algorithm, but it is not inherently derived from the data, but rather... | Are Random Forest and Boosting parametric or non-parametric?
In statistical sense, the model is parametric, if parameters are learned or inferred based on the data. A tree in this sense is nonparametric. Of course the tree depth is a parameter of the algorithm, |
13,448 | Are Random Forest and Boosting parametric or non-parametric? | I would have thought that the fact that a given training set only has one possible set of computed parameters would also determine if the model is parametric. This is the case in boosting, logistic regression, linear regression and models of this sort which would mostly be considered parametric whereas the parameters e... | Are Random Forest and Boosting parametric or non-parametric? | I would have thought that the fact that a given training set only has one possible set of computed parameters would also determine if the model is parametric. This is the case in boosting, logistic re | Are Random Forest and Boosting parametric or non-parametric?
I would have thought that the fact that a given training set only has one possible set of computed parameters would also determine if the model is parametric. This is the case in boosting, logistic regression, linear regression and models of this sort which w... | Are Random Forest and Boosting parametric or non-parametric?
I would have thought that the fact that a given training set only has one possible set of computed parameters would also determine if the model is parametric. This is the case in boosting, logistic re |
13,449 | What is Recurrent Reinforcement Learning | What is a "recurrent reinforcement learning"?
Recurrent reinforcement learning (RRL) was first introduced for training neural network trading systems in 1996. "Recurrent" means that previous output is fed into the model as a part of input. It was soon extended to trading in a FX market.
The RRL technique has been foun... | What is Recurrent Reinforcement Learning | What is a "recurrent reinforcement learning"?
Recurrent reinforcement learning (RRL) was first introduced for training neural network trading systems in 1996. "Recurrent" means that previous output i | What is Recurrent Reinforcement Learning
What is a "recurrent reinforcement learning"?
Recurrent reinforcement learning (RRL) was first introduced for training neural network trading systems in 1996. "Recurrent" means that previous output is fed into the model as a part of input. It was soon extended to trading in a F... | What is Recurrent Reinforcement Learning
What is a "recurrent reinforcement learning"?
Recurrent reinforcement learning (RRL) was first introduced for training neural network trading systems in 1996. "Recurrent" means that previous output i |
13,450 | What is Recurrent Reinforcement Learning | The distinction of (Deep) Recurrent RL, is that the function mapping the agents observations to its output action is a Recurrent Neural Network.
A Recurrent Neural Network is a type of neural network that processes each observation sequentially, in the same way for each time step.
Original paper: Deep Recurrent Q-Lear... | What is Recurrent Reinforcement Learning | The distinction of (Deep) Recurrent RL, is that the function mapping the agents observations to its output action is a Recurrent Neural Network.
A Recurrent Neural Network is a type of neural network | What is Recurrent Reinforcement Learning
The distinction of (Deep) Recurrent RL, is that the function mapping the agents observations to its output action is a Recurrent Neural Network.
A Recurrent Neural Network is a type of neural network that processes each observation sequentially, in the same way for each time st... | What is Recurrent Reinforcement Learning
The distinction of (Deep) Recurrent RL, is that the function mapping the agents observations to its output action is a Recurrent Neural Network.
A Recurrent Neural Network is a type of neural network |
13,451 | Variance-covariance matrix in lmer | Mixed models are (generalized versions of) variance components models. You write down the fixed effects part, add error terms that may be common for some groups of observations, add link function if needed, and put this into a likelihood maximizer.
The various variance structures you are describing, however, are the wo... | Variance-covariance matrix in lmer | Mixed models are (generalized versions of) variance components models. You write down the fixed effects part, add error terms that may be common for some groups of observations, add link function if n | Variance-covariance matrix in lmer
Mixed models are (generalized versions of) variance components models. You write down the fixed effects part, add error terms that may be common for some groups of observations, add link function if needed, and put this into a likelihood maximizer.
The various variance structures you ... | Variance-covariance matrix in lmer
Mixed models are (generalized versions of) variance components models. You write down the fixed effects part, add error terms that may be common for some groups of observations, add link function if n |
13,452 | Variance-covariance matrix in lmer | The FlexLamba branch of lmer provides such a functionality.
See https://github.com/lme4/lme4/issues/224 for examples how to implement a specific structure of errors or random effects. | Variance-covariance matrix in lmer | The FlexLamba branch of lmer provides such a functionality.
See https://github.com/lme4/lme4/issues/224 for examples how to implement a specific structure of errors or random effects. | Variance-covariance matrix in lmer
The FlexLamba branch of lmer provides such a functionality.
See https://github.com/lme4/lme4/issues/224 for examples how to implement a specific structure of errors or random effects. | Variance-covariance matrix in lmer
The FlexLamba branch of lmer provides such a functionality.
See https://github.com/lme4/lme4/issues/224 for examples how to implement a specific structure of errors or random effects. |
13,453 | Variance-covariance matrix in lmer | To my knowledge lmer is not having an "easy" way to address this. Also given that in most cases lmer makes heavy use of sparse matrices for Cholesky factorization I would find it unlikely that it allows for totally unstructured VCV's.
To your address your question on "default structure": there is not a concept of defa... | Variance-covariance matrix in lmer | To my knowledge lmer is not having an "easy" way to address this. Also given that in most cases lmer makes heavy use of sparse matrices for Cholesky factorization I would find it unlikely that it allo | Variance-covariance matrix in lmer
To my knowledge lmer is not having an "easy" way to address this. Also given that in most cases lmer makes heavy use of sparse matrices for Cholesky factorization I would find it unlikely that it allows for totally unstructured VCV's.
To your address your question on "default structu... | Variance-covariance matrix in lmer
To my knowledge lmer is not having an "easy" way to address this. Also given that in most cases lmer makes heavy use of sparse matrices for Cholesky factorization I would find it unlikely that it allo |
13,454 | Is there an unbiased estimator of the Hellinger distance between two distributions? | No unbiased estimator either of $\mathfrak{H}$ or of $\mathfrak{H}^2$ exists for $f$ from any reasonably broad nonparametric class of distributions.
We can show this with the beautifully simple argument of
Bickel and Lehmann (1969). Unbiased estimation in convex families. The Annals of Mathematical Statistics, 40 (5) ... | Is there an unbiased estimator of the Hellinger distance between two distributions? | No unbiased estimator either of $\mathfrak{H}$ or of $\mathfrak{H}^2$ exists for $f$ from any reasonably broad nonparametric class of distributions.
We can show this with the beautifully simple argume | Is there an unbiased estimator of the Hellinger distance between two distributions?
No unbiased estimator either of $\mathfrak{H}$ or of $\mathfrak{H}^2$ exists for $f$ from any reasonably broad nonparametric class of distributions.
We can show this with the beautifully simple argument of
Bickel and Lehmann (1969). Un... | Is there an unbiased estimator of the Hellinger distance between two distributions?
No unbiased estimator either of $\mathfrak{H}$ or of $\mathfrak{H}^2$ exists for $f$ from any reasonably broad nonparametric class of distributions.
We can show this with the beautifully simple argume |
13,455 | Is there an unbiased estimator of the Hellinger distance between two distributions? | I don't know how to construct (if it exists) an unbiased estimator of the Hellinger distance. It seems possible to construct a consistent estimator. We have some fixed known density $f_0$, and a random sample $X_1,\dots,X_n$ from a density $f>0$. We want to estimate
$$
H(f,f_0) = \sqrt{1 - \int_\mathscr{X} \sqrt{f(x)f_... | Is there an unbiased estimator of the Hellinger distance between two distributions? | I don't know how to construct (if it exists) an unbiased estimator of the Hellinger distance. It seems possible to construct a consistent estimator. We have some fixed known density $f_0$, and a rando | Is there an unbiased estimator of the Hellinger distance between two distributions?
I don't know how to construct (if it exists) an unbiased estimator of the Hellinger distance. It seems possible to construct a consistent estimator. We have some fixed known density $f_0$, and a random sample $X_1,\dots,X_n$ from a dens... | Is there an unbiased estimator of the Hellinger distance between two distributions?
I don't know how to construct (if it exists) an unbiased estimator of the Hellinger distance. It seems possible to construct a consistent estimator. We have some fixed known density $f_0$, and a rando |
13,456 | How can I predict values from new inputs of a linear model in R? | If you want the predicted values for train_x = 1, 2, and 3, use predict(mod, data.frame(train_x = c(1, 2, 3))). | How can I predict values from new inputs of a linear model in R? | If you want the predicted values for train_x = 1, 2, and 3, use predict(mod, data.frame(train_x = c(1, 2, 3))). | How can I predict values from new inputs of a linear model in R?
If you want the predicted values for train_x = 1, 2, and 3, use predict(mod, data.frame(train_x = c(1, 2, 3))). | How can I predict values from new inputs of a linear model in R?
If you want the predicted values for train_x = 1, 2, and 3, use predict(mod, data.frame(train_x = c(1, 2, 3))). |
13,457 | How to use weights in function lm in R? | I think R help page of lm answers your question pretty well. The only requirement for weights is that the vector supplied must be the same length as the data. You can even supply only the name of the variable in the data set, R will take care of the rest, NA management, etc. You can also use formulas in the weight argu... | How to use weights in function lm in R? | I think R help page of lm answers your question pretty well. The only requirement for weights is that the vector supplied must be the same length as the data. You can even supply only the name of the | How to use weights in function lm in R?
I think R help page of lm answers your question pretty well. The only requirement for weights is that the vector supplied must be the same length as the data. You can even supply only the name of the variable in the data set, R will take care of the rest, NA management, etc. You ... | How to use weights in function lm in R?
I think R help page of lm answers your question pretty well. The only requirement for weights is that the vector supplied must be the same length as the data. You can even supply only the name of the |
13,458 | How to use weights in function lm in R? | What you suggest should work. See if this makes sense:
lm(c(8000, 50000, 116000) ~ c(6, 7, 8))
lm(c(8000, 50000, 116000) ~ c(6, 7, 8), weight = c(123, 123, 246))
lm(c(8000, 50000, 116000, 116000) ~ c(6, 7, 8, 8))
The second line produces the same intercept and slope as the third line (distinct from the first line's r... | How to use weights in function lm in R? | What you suggest should work. See if this makes sense:
lm(c(8000, 50000, 116000) ~ c(6, 7, 8))
lm(c(8000, 50000, 116000) ~ c(6, 7, 8), weight = c(123, 123, 246))
lm(c(8000, 50000, 116000, 116000) ~ c | How to use weights in function lm in R?
What you suggest should work. See if this makes sense:
lm(c(8000, 50000, 116000) ~ c(6, 7, 8))
lm(c(8000, 50000, 116000) ~ c(6, 7, 8), weight = c(123, 123, 246))
lm(c(8000, 50000, 116000, 116000) ~ c(6, 7, 8, 8))
The second line produces the same intercept and slope as the thir... | How to use weights in function lm in R?
What you suggest should work. See if this makes sense:
lm(c(8000, 50000, 116000) ~ c(6, 7, 8))
lm(c(8000, 50000, 116000) ~ c(6, 7, 8), weight = c(123, 123, 246))
lm(c(8000, 50000, 116000, 116000) ~ c |
13,459 | Why is LogLoss preferred over other proper scoring rules? | Arguments for the log score
On the one hand, as kjetil b halvorsen writes, the log loss is just a reformulation of the log likelihood, which statisticians are very used to maximizing, so it is simply very natural as a KPIs. (A somewhat more common convention is to minimize the score, in which case, one takes the negati... | Why is LogLoss preferred over other proper scoring rules? | Arguments for the log score
On the one hand, as kjetil b halvorsen writes, the log loss is just a reformulation of the log likelihood, which statisticians are very used to maximizing, so it is simply | Why is LogLoss preferred over other proper scoring rules?
Arguments for the log score
On the one hand, as kjetil b halvorsen writes, the log loss is just a reformulation of the log likelihood, which statisticians are very used to maximizing, so it is simply very natural as a KPIs. (A somewhat more common convention is ... | Why is LogLoss preferred over other proper scoring rules?
Arguments for the log score
On the one hand, as kjetil b halvorsen writes, the log loss is just a reformulation of the log likelihood, which statisticians are very used to maximizing, so it is simply |
13,460 | At What Level is a $\chi^2$ test Mathematically Identical to a $z$-test of Proportions? | Let us have a 2x2 frequency table where columns are two groups of respondents and rows are the two responses "Yes" and "No". And we've turned the frequencies into the proportions within group, i.e. into the vertical profiles:
Gr1 Gr2 Total
Yes p1 p2 p
No q1 q2 q
--------------
100... | At What Level is a $\chi^2$ test Mathematically Identical to a $z$-test of Proportions? | Let us have a 2x2 frequency table where columns are two groups of respondents and rows are the two responses "Yes" and "No". And we've turned the frequencies into the proportions within group, i.e. in | At What Level is a $\chi^2$ test Mathematically Identical to a $z$-test of Proportions?
Let us have a 2x2 frequency table where columns are two groups of respondents and rows are the two responses "Yes" and "No". And we've turned the frequencies into the proportions within group, i.e. into the vertical profiles:
... | At What Level is a $\chi^2$ test Mathematically Identical to a $z$-test of Proportions?
Let us have a 2x2 frequency table where columns are two groups of respondents and rows are the two responses "Yes" and "No". And we've turned the frequencies into the proportions within group, i.e. in |
13,461 | How to achieve strictly positive forecasts? | With the forecast package for R, simply set lambda=0 when fitting a model. For example:
fit <- auto.arima(x, lambda=0)
forecast(fit)
Many of the functions in the package allow the lambda argument. When the lambda argument is specified, a Box-Cox transformation is used. The value $\lambda=0$ specifies a log transformat... | How to achieve strictly positive forecasts? | With the forecast package for R, simply set lambda=0 when fitting a model. For example:
fit <- auto.arima(x, lambda=0)
forecast(fit)
Many of the functions in the package allow the lambda argument. Wh | How to achieve strictly positive forecasts?
With the forecast package for R, simply set lambda=0 when fitting a model. For example:
fit <- auto.arima(x, lambda=0)
forecast(fit)
Many of the functions in the package allow the lambda argument. When the lambda argument is specified, a Box-Cox transformation is used. The v... | How to achieve strictly positive forecasts?
With the forecast package for R, simply set lambda=0 when fitting a model. For example:
fit <- auto.arima(x, lambda=0)
forecast(fit)
Many of the functions in the package allow the lambda argument. Wh |
13,462 | Can I convert a covariance matrix into uncertainties for variables? | There is no single number that encompasses all of the covariance information - there are 6 pieces of information, so you'd always need 6 numbers.
However there are a number of things you could consider doing.
Firstly, the error (variance) in any particular direction $i$, is given by
$\sigma_i^2 = \mathbf{e}_i ^ \top \S... | Can I convert a covariance matrix into uncertainties for variables? | There is no single number that encompasses all of the covariance information - there are 6 pieces of information, so you'd always need 6 numbers.
However there are a number of things you could conside | Can I convert a covariance matrix into uncertainties for variables?
There is no single number that encompasses all of the covariance information - there are 6 pieces of information, so you'd always need 6 numbers.
However there are a number of things you could consider doing.
Firstly, the error (variance) in any partic... | Can I convert a covariance matrix into uncertainties for variables?
There is no single number that encompasses all of the covariance information - there are 6 pieces of information, so you'd always need 6 numbers.
However there are a number of things you could conside |
13,463 | Survival analysis: continuous vs discrete time | The choice of the survival model should be guided by the underlying phenomenon. In this case it appears to be continuous, even if the data is collected in a somewhat discrete manner. A resolution of one month would be just fine over a 5-year period.
However, the large number of ties at 6 and 12 months makes one wonder ... | Survival analysis: continuous vs discrete time | The choice of the survival model should be guided by the underlying phenomenon. In this case it appears to be continuous, even if the data is collected in a somewhat discrete manner. A resolution of o | Survival analysis: continuous vs discrete time
The choice of the survival model should be guided by the underlying phenomenon. In this case it appears to be continuous, even if the data is collected in a somewhat discrete manner. A resolution of one month would be just fine over a 5-year period.
However, the large numb... | Survival analysis: continuous vs discrete time
The choice of the survival model should be guided by the underlying phenomenon. In this case it appears to be continuous, even if the data is collected in a somewhat discrete manner. A resolution of o |
13,464 | Survival analysis: continuous vs discrete time | I suspect if you use continuous time models you will want to use interval censoring, reflecting the fact that you don't know the exact time of failure, just an interval in which the failure ocurred. If you fit parametric regression models with interval censoring using maximum likelihhod the tied survival times is not ... | Survival analysis: continuous vs discrete time | I suspect if you use continuous time models you will want to use interval censoring, reflecting the fact that you don't know the exact time of failure, just an interval in which the failure ocurred. | Survival analysis: continuous vs discrete time
I suspect if you use continuous time models you will want to use interval censoring, reflecting the fact that you don't know the exact time of failure, just an interval in which the failure ocurred. If you fit parametric regression models with interval censoring using max... | Survival analysis: continuous vs discrete time
I suspect if you use continuous time models you will want to use interval censoring, reflecting the fact that you don't know the exact time of failure, just an interval in which the failure ocurred. |
13,465 | Survival analysis: continuous vs discrete time | There will be tied survival times in most analysis, but big, clear chunks of ties at particular events is troubling. I would think long and hard about the study itself, how its collecting data, etc.
Because, outside of some methodological needs to use one type of time or the other, how you model survival should depend ... | Survival analysis: continuous vs discrete time | There will be tied survival times in most analysis, but big, clear chunks of ties at particular events is troubling. I would think long and hard about the study itself, how its collecting data, etc.
B | Survival analysis: continuous vs discrete time
There will be tied survival times in most analysis, but big, clear chunks of ties at particular events is troubling. I would think long and hard about the study itself, how its collecting data, etc.
Because, outside of some methodological needs to use one type of time or t... | Survival analysis: continuous vs discrete time
There will be tied survival times in most analysis, but big, clear chunks of ties at particular events is troubling. I would think long and hard about the study itself, how its collecting data, etc.
B |
13,466 | Survival analysis: continuous vs discrete time | If you have covariates that vary over time for the some individuals (e.g. family income may vary in your example over the lifetime of a child), survival models (parametric and the cox model) require you to slice up the data into discrete intervals defined by the varying covariates.
I found this pdf of lecture notes by ... | Survival analysis: continuous vs discrete time | If you have covariates that vary over time for the some individuals (e.g. family income may vary in your example over the lifetime of a child), survival models (parametric and the cox model) require y | Survival analysis: continuous vs discrete time
If you have covariates that vary over time for the some individuals (e.g. family income may vary in your example over the lifetime of a child), survival models (parametric and the cox model) require you to slice up the data into discrete intervals defined by the varying co... | Survival analysis: continuous vs discrete time
If you have covariates that vary over time for the some individuals (e.g. family income may vary in your example over the lifetime of a child), survival models (parametric and the cox model) require y |
13,467 | Interpreting the drop1 output in R | drop1 gives you a comparison of models based on the AIC criterion, and when using the option test="F" you add a "type II ANOVA" to it, as explained in the help files. As long as you only have continuous variables, this table is exactly equivalent to summary(lm1), as the F-values are just those T-values squared. P-valu... | Interpreting the drop1 output in R | drop1 gives you a comparison of models based on the AIC criterion, and when using the option test="F" you add a "type II ANOVA" to it, as explained in the help files. As long as you only have continuo | Interpreting the drop1 output in R
drop1 gives you a comparison of models based on the AIC criterion, and when using the option test="F" you add a "type II ANOVA" to it, as explained in the help files. As long as you only have continuous variables, this table is exactly equivalent to summary(lm1), as the F-values are ... | Interpreting the drop1 output in R
drop1 gives you a comparison of models based on the AIC criterion, and when using the option test="F" you add a "type II ANOVA" to it, as explained in the help files. As long as you only have continuo |
13,468 | Interpreting the drop1 output in R | For reference, these are the values that are included in the table:
Df refers to Degrees of freedom, "the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary."
The Sum of Sq column refers to the sum of squares (or more precisely sum of squared deviations)... | Interpreting the drop1 output in R | For reference, these are the values that are included in the table:
Df refers to Degrees of freedom, "the number of degrees of freedom is the number of values in the final calculation of a statistic t | Interpreting the drop1 output in R
For reference, these are the values that are included in the table:
Df refers to Degrees of freedom, "the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary."
The Sum of Sq column refers to the sum of squares (or more p... | Interpreting the drop1 output in R
For reference, these are the values that are included in the table:
Df refers to Degrees of freedom, "the number of degrees of freedom is the number of values in the final calculation of a statistic t |
13,469 | Brier Score and extreme class imbalance | If there is extreme class imbalance (e.g. 5 positive cases vs 1,000 negative cases), how does the Brier score ensure that we select the model that gives us the best performance regarding high probability forecasts for the 5 positive cases? As we do not care if the negative cases have predictions near 0 or 0.5 as long a... | Brier Score and extreme class imbalance | If there is extreme class imbalance (e.g. 5 positive cases vs 1,000 negative cases), how does the Brier score ensure that we select the model that gives us the best performance regarding high probabil | Brier Score and extreme class imbalance
If there is extreme class imbalance (e.g. 5 positive cases vs 1,000 negative cases), how does the Brier score ensure that we select the model that gives us the best performance regarding high probability forecasts for the 5 positive cases? As we do not care if the negative cases ... | Brier Score and extreme class imbalance
If there is extreme class imbalance (e.g. 5 positive cases vs 1,000 negative cases), how does the Brier score ensure that we select the model that gives us the best performance regarding high probabil |
13,470 | Brier Score and extreme class imbalance | The paper "Class Probability Estimates are Unreliable for Imbalanced Data
(and How to Fix Them)" (Wallace & Dahabreh 2012) argues that the Brier score as is fails to account for poor calibrations in minority classes. They propose a stratified Brier score:
$$BS^+ = \frac{\sum_{y_i=1}\left(y_i- \hat{P}\left\{y_i|x_i\righ... | Brier Score and extreme class imbalance | The paper "Class Probability Estimates are Unreliable for Imbalanced Data
(and How to Fix Them)" (Wallace & Dahabreh 2012) argues that the Brier score as is fails to account for poor calibrations in m | Brier Score and extreme class imbalance
The paper "Class Probability Estimates are Unreliable for Imbalanced Data
(and How to Fix Them)" (Wallace & Dahabreh 2012) argues that the Brier score as is fails to account for poor calibrations in minority classes. They propose a stratified Brier score:
$$BS^+ = \frac{\sum_{y_i... | Brier Score and extreme class imbalance
The paper "Class Probability Estimates are Unreliable for Imbalanced Data
(and How to Fix Them)" (Wallace & Dahabreh 2012) argues that the Brier score as is fails to account for poor calibrations in m |
13,471 | Brier Score and extreme class imbalance | If there is extreme class imbalance (e.g. 5 positive cases vs 1,000
negative cases), how does the Brier score ensure that we select the
model that gives us the best performance regarding high probability
forecasts for the 5 positive cases? As we do not care if the negative
cases have predictions near 0 or 0.5 as long a... | Brier Score and extreme class imbalance | If there is extreme class imbalance (e.g. 5 positive cases vs 1,000
negative cases), how does the Brier score ensure that we select the
model that gives us the best performance regarding high probabil | Brier Score and extreme class imbalance
If there is extreme class imbalance (e.g. 5 positive cases vs 1,000
negative cases), how does the Brier score ensure that we select the
model that gives us the best performance regarding high probability
forecasts for the 5 positive cases? As we do not care if the negative
cases ... | Brier Score and extreme class imbalance
If there is extreme class imbalance (e.g. 5 positive cases vs 1,000
negative cases), how does the Brier score ensure that we select the
model that gives us the best performance regarding high probabil |
13,472 | How to interpret PCA on time-series data? | Q1: What is the connection between PC time series and "maximum variance"?
The data that they are analyzing are $\hat t$ data points for each of the $n$ neurons, so one can think about that as $\hat t$ data points in the $n$-dimensional space $\mathbb R^n$. It is "a cloud of points", so performing PCA amounts to finding... | How to interpret PCA on time-series data? | Q1: What is the connection between PC time series and "maximum variance"?
The data that they are analyzing are $\hat t$ data points for each of the $n$ neurons, so one can think about that as $\hat t$ | How to interpret PCA on time-series data?
Q1: What is the connection between PC time series and "maximum variance"?
The data that they are analyzing are $\hat t$ data points for each of the $n$ neurons, so one can think about that as $\hat t$ data points in the $n$-dimensional space $\mathbb R^n$. It is "a cloud of poi... | How to interpret PCA on time-series data?
Q1: What is the connection between PC time series and "maximum variance"?
The data that they are analyzing are $\hat t$ data points for each of the $n$ neurons, so one can think about that as $\hat t$ |
13,473 | How to interpret PCA on time-series data? | With respect to the first question. Consider the whole time series through a particular voxel to be a single draw from a multivariate distribution. We can now think of this as a multivariate vector much like any other that we might apply PCA to. The first $p$ columns of $\bf V$ are then the eigen-timecourses which, whe... | How to interpret PCA on time-series data? | With respect to the first question. Consider the whole time series through a particular voxel to be a single draw from a multivariate distribution. We can now think of this as a multivariate vector mu | How to interpret PCA on time-series data?
With respect to the first question. Consider the whole time series through a particular voxel to be a single draw from a multivariate distribution. We can now think of this as a multivariate vector much like any other that we might apply PCA to. The first $p$ columns of $\bf V$... | How to interpret PCA on time-series data?
With respect to the first question. Consider the whole time series through a particular voxel to be a single draw from a multivariate distribution. We can now think of this as a multivariate vector mu |
13,474 | Calculating standard error after a log-transform | Your main problem with the initial calculation is there's no good reason why $e^{\text{sd}(\log(Y))}$ should be like $\text{sd}(Y)$. It's generally quite different.
In some situations, you can compute a rough approximation of $\text{sd}(Y)$ from $\text{sd}(\log(Y))$ via Taylor expansion.
$$\text{Var}(g(X))\approx \left... | Calculating standard error after a log-transform | Your main problem with the initial calculation is there's no good reason why $e^{\text{sd}(\log(Y))}$ should be like $\text{sd}(Y)$. It's generally quite different.
In some situations, you can compute | Calculating standard error after a log-transform
Your main problem with the initial calculation is there's no good reason why $e^{\text{sd}(\log(Y))}$ should be like $\text{sd}(Y)$. It's generally quite different.
In some situations, you can compute a rough approximation of $\text{sd}(Y)$ from $\text{sd}(\log(Y))$ via ... | Calculating standard error after a log-transform
Your main problem with the initial calculation is there's no good reason why $e^{\text{sd}(\log(Y))}$ should be like $\text{sd}(Y)$. It's generally quite different.
In some situations, you can compute |
13,475 | Calculating standard error after a log-transform | It sounds like you effectively want the geometric standard error, akin to the geometric mean exp(mean(log(x))).
While it might seem reasonable to compute that as:
exp(sd(log(x)/sqrt(n-1)))
You and others have already pointed out that that isn't correct for a few reasons. Instead, use:
exp(mean(log(x))) * (sd(log(x))/s... | Calculating standard error after a log-transform | It sounds like you effectively want the geometric standard error, akin to the geometric mean exp(mean(log(x))).
While it might seem reasonable to compute that as:
exp(sd(log(x)/sqrt(n-1)))
You and ot | Calculating standard error after a log-transform
It sounds like you effectively want the geometric standard error, akin to the geometric mean exp(mean(log(x))).
While it might seem reasonable to compute that as:
exp(sd(log(x)/sqrt(n-1)))
You and others have already pointed out that that isn't correct for a few reasons... | Calculating standard error after a log-transform
It sounds like you effectively want the geometric standard error, akin to the geometric mean exp(mean(log(x))).
While it might seem reasonable to compute that as:
exp(sd(log(x)/sqrt(n-1)))
You and ot |
13,476 | Are "random sample" and "iid random variable" synonyms? | You don't say what the other statistics book is, but I'd guess that it is a
book (or section) about finite population sampling.
When you sample random variables, i.e. when you consider a set
$X_1,\dots,X_n$ of $n$ random variables, you know that if they are
independent, $f(x_1,\dots,x_n)=f(x_1)\cdots f(x_n)$, and ident... | Are "random sample" and "iid random variable" synonyms? | You don't say what the other statistics book is, but I'd guess that it is a
book (or section) about finite population sampling.
When you sample random variables, i.e. when you consider a set
$X_1,\dot | Are "random sample" and "iid random variable" synonyms?
You don't say what the other statistics book is, but I'd guess that it is a
book (or section) about finite population sampling.
When you sample random variables, i.e. when you consider a set
$X_1,\dots,X_n$ of $n$ random variables, you know that if they are
indepe... | Are "random sample" and "iid random variable" synonyms?
You don't say what the other statistics book is, but I'd guess that it is a
book (or section) about finite population sampling.
When you sample random variables, i.e. when you consider a set
$X_1,\dot |
13,477 | Are "random sample" and "iid random variable" synonyms? | I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)
Just think of this intuitively, random sample is a set of random values. In general, each one of the values may either be identically or differently distributed. $i.i.d.$ sam... | Are "random sample" and "iid random variable" synonyms? | I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)
Just think of this intuitively, random sample is a set | Are "random sample" and "iid random variable" synonyms?
I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)
Just think of this intuitively, random sample is a set of random values. In general, each one of the values may either... | Are "random sample" and "iid random variable" synonyms?
I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)
Just think of this intuitively, random sample is a set |
13,478 | Are "random sample" and "iid random variable" synonyms? | A Random Variable usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. The random phenomenon may produce outcomes that have numerical values captured by the random variable --e.g. number of heads in 10 tosses of a coin or incomes/heights etc in a sample -- but that is no... | Are "random sample" and "iid random variable" synonyms? | A Random Variable usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. The random phenomenon may produce outcomes that have numerical values captured b | Are "random sample" and "iid random variable" synonyms?
A Random Variable usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. The random phenomenon may produce outcomes that have numerical values captured by the random variable --e.g. number of heads in 10 tosses of a c... | Are "random sample" and "iid random variable" synonyms?
A Random Variable usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. The random phenomenon may produce outcomes that have numerical values captured b |
13,479 | Are "random sample" and "iid random variable" synonyms? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
A random sample is a realization of a sequence of random variables. Those random variables may be i.i.d or not... | Are "random sample" and "iid random variable" synonyms? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
| Are "random sample" and "iid random variable" synonyms?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
A random sample is a realization of a sequence of rand... | Are "random sample" and "iid random variable" synonyms?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
|
13,480 | Error in normal approximation to a uniform sum distribution | Let $U_1, U_2,\dots$ be iid $\mathcal U(-b,b)$ random variables and consider the normalized sum
$$
S_n = \frac{\sqrt{3} \sum_{i=1}^n U_i}{b \sqrt{n}} \>,
$$
and the associated $\sup$ norm
$$
\delta_n = \sup_{x\in\mathbb R} |F_n(x) - \Phi(x)| \>,
$$
where $F_n$ is the distribution of $S_n$.
Lemma 1 (Uspensky): The follo... | Error in normal approximation to a uniform sum distribution | Let $U_1, U_2,\dots$ be iid $\mathcal U(-b,b)$ random variables and consider the normalized sum
$$
S_n = \frac{\sqrt{3} \sum_{i=1}^n U_i}{b \sqrt{n}} \>,
$$
and the associated $\sup$ norm
$$
\delta_n | Error in normal approximation to a uniform sum distribution
Let $U_1, U_2,\dots$ be iid $\mathcal U(-b,b)$ random variables and consider the normalized sum
$$
S_n = \frac{\sqrt{3} \sum_{i=1}^n U_i}{b \sqrt{n}} \>,
$$
and the associated $\sup$ norm
$$
\delta_n = \sup_{x\in\mathbb R} |F_n(x) - \Phi(x)| \>,
$$
where $F_n$... | Error in normal approximation to a uniform sum distribution
Let $U_1, U_2,\dots$ be iid $\mathcal U(-b,b)$ random variables and consider the normalized sum
$$
S_n = \frac{\sqrt{3} \sum_{i=1}^n U_i}{b \sqrt{n}} \>,
$$
and the associated $\sup$ norm
$$
\delta_n |
13,481 | How to calculate perplexity of a holdout with Latent Dirichlet Allocation? | This is indeed something often glossed over.
Some people are doing something a bit cheeky: holding out a proportion of the words in each document, and giving using predictive probabilities of these held-out words given the document-topic mixtures as well as the topic-word mixtures. This is obviously not ideal as it doe... | How to calculate perplexity of a holdout with Latent Dirichlet Allocation? | This is indeed something often glossed over.
Some people are doing something a bit cheeky: holding out a proportion of the words in each document, and giving using predictive probabilities of these he | How to calculate perplexity of a holdout with Latent Dirichlet Allocation?
This is indeed something often glossed over.
Some people are doing something a bit cheeky: holding out a proportion of the words in each document, and giving using predictive probabilities of these held-out words given the document-topic mixture... | How to calculate perplexity of a holdout with Latent Dirichlet Allocation?
This is indeed something often glossed over.
Some people are doing something a bit cheeky: holding out a proportion of the words in each document, and giving using predictive probabilities of these he |
13,482 | How to calculate perplexity of a holdout with Latent Dirichlet Allocation? | We know that parameters of LDA are estimated through Variational Inference. So
$\log p(w|\alpha, \beta) = E[\log p(\theta,z,w|\alpha,\beta)]-E[\log q(\theta,z)] + D(q(\theta,z)||p(\theta,z))$.
If your variational distribution is enough equal to the original distribution, then $D(q(\theta,z)||p(\theta,z)) = 0$. So, $\... | How to calculate perplexity of a holdout with Latent Dirichlet Allocation? | We know that parameters of LDA are estimated through Variational Inference. So
$\log p(w|\alpha, \beta) = E[\log p(\theta,z,w|\alpha,\beta)]-E[\log q(\theta,z)] + D(q(\theta,z)||p(\theta,z))$.
If yo | How to calculate perplexity of a holdout with Latent Dirichlet Allocation?
We know that parameters of LDA are estimated through Variational Inference. So
$\log p(w|\alpha, \beta) = E[\log p(\theta,z,w|\alpha,\beta)]-E[\log q(\theta,z)] + D(q(\theta,z)||p(\theta,z))$.
If your variational distribution is enough equal t... | How to calculate perplexity of a holdout with Latent Dirichlet Allocation?
We know that parameters of LDA are estimated through Variational Inference. So
$\log p(w|\alpha, \beta) = E[\log p(\theta,z,w|\alpha,\beta)]-E[\log q(\theta,z)] + D(q(\theta,z)||p(\theta,z))$.
If yo |
13,483 | How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation | This is a great question! Not sure this is a full answer, however, I drop these few lines in case it helps.
It seems that Yucel and Demirtas (2010) refer to an older paper published in the JCGS, Computational strategies for multivariate linear mixed-effects models with missing values, which uses an hybrid EM/Fisher sco... | How to combine confidence intervals for a variance component of a mixed-effects model when using mul | This is a great question! Not sure this is a full answer, however, I drop these few lines in case it helps.
It seems that Yucel and Demirtas (2010) refer to an older paper published in the JCGS, Compu | How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation
This is a great question! Not sure this is a full answer, however, I drop these few lines in case it helps.
It seems that Yucel and Demirtas (2010) refer to an older paper published in the JCGS, Computat... | How to combine confidence intervals for a variance component of a mixed-effects model when using mul
This is a great question! Not sure this is a full answer, however, I drop these few lines in case it helps.
It seems that Yucel and Demirtas (2010) refer to an older paper published in the JCGS, Compu |
13,484 | How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation | Repeated comment from above:
i'm not sure that a proper analytical solution to this problem even exists. I've looked at some additional literature, but this problem is elegantly overlooked everywhere. I've also noticed that Yucel & Demirtas (in the article i mentioned, page 798) write:
These multiply imputed datasets ... | How to combine confidence intervals for a variance component of a mixed-effects model when using mul | Repeated comment from above:
i'm not sure that a proper analytical solution to this problem even exists. I've looked at some additional literature, but this problem is elegantly overlooked everywhere. | How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation
Repeated comment from above:
i'm not sure that a proper analytical solution to this problem even exists. I've looked at some additional literature, but this problem is elegantly overlooked everywhere. I'... | How to combine confidence intervals for a variance component of a mixed-effects model when using mul
Repeated comment from above:
i'm not sure that a proper analytical solution to this problem even exists. I've looked at some additional literature, but this problem is elegantly overlooked everywhere. |
13,485 | How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation | Disclaimer: This idea might be foolish & I'm not going to pretend to understand the theoretical implications of what I'm proposing.
"Suggestion" : Why don't you simply impute 100 (I know you normally do 5) datasets, run the lme4 or nmle, get the confidence intervals (you have 100 of them) and then:
Using a small int... | How to combine confidence intervals for a variance component of a mixed-effects model when using mul | Disclaimer: This idea might be foolish & I'm not going to pretend to understand the theoretical implications of what I'm proposing.
"Suggestion" : Why don't you simply impute 100 (I know you normal | How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation
Disclaimer: This idea might be foolish & I'm not going to pretend to understand the theoretical implications of what I'm proposing.
"Suggestion" : Why don't you simply impute 100 (I know you normally ... | How to combine confidence intervals for a variance component of a mixed-effects model when using mul
Disclaimer: This idea might be foolish & I'm not going to pretend to understand the theoretical implications of what I'm proposing.
"Suggestion" : Why don't you simply impute 100 (I know you normal |
13,486 | What is the difference between Markov chains and Markov processes? | From the preface to the first edition of "Markov Chains and Stochastic Stability" by Meyn and Tweedie:
We deal here with Markov Chains. Despite the initial attempts by Doob and Chung [99,71] to reserve this term for systems evolving on countable spaces with both discrete and continuous time parameters, usage seems to ... | What is the difference between Markov chains and Markov processes? | From the preface to the first edition of "Markov Chains and Stochastic Stability" by Meyn and Tweedie:
We deal here with Markov Chains. Despite the initial attempts by Doob and Chung [99,71] to reser | What is the difference between Markov chains and Markov processes?
From the preface to the first edition of "Markov Chains and Stochastic Stability" by Meyn and Tweedie:
We deal here with Markov Chains. Despite the initial attempts by Doob and Chung [99,71] to reserve this term for systems evolving on countable spaces... | What is the difference between Markov chains and Markov processes?
From the preface to the first edition of "Markov Chains and Stochastic Stability" by Meyn and Tweedie:
We deal here with Markov Chains. Despite the initial attempts by Doob and Chung [99,71] to reser |
13,487 | What is the difference between Markov chains and Markov processes? | One method of classification of stochastic processes is
based on the nature of the time parameter(discrete or continuous) and state space(discrete or continuous). This leads to four categories of stochastic processes.
If the state space of stochastic process is discrete, whether the time parameter is discrete or contin... | What is the difference between Markov chains and Markov processes? | One method of classification of stochastic processes is
based on the nature of the time parameter(discrete or continuous) and state space(discrete or continuous). This leads to four categories of stoc | What is the difference between Markov chains and Markov processes?
One method of classification of stochastic processes is
based on the nature of the time parameter(discrete or continuous) and state space(discrete or continuous). This leads to four categories of stochastic processes.
If the state space of stochastic pr... | What is the difference between Markov chains and Markov processes?
One method of classification of stochastic processes is
based on the nature of the time parameter(discrete or continuous) and state space(discrete or continuous). This leads to four categories of stoc |
13,488 | What is the origin of the autoencoder neural networks? | According to the history provided in Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks (2015), auto-encoders were proposed as a method for unsupervised pre-training in Ballard, "Modular learning in neural networks," Proceedings AAAI (1987). It's not clear if that's the first time auto-encode... | What is the origin of the autoencoder neural networks? | According to the history provided in Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks (2015), auto-encoders were proposed as a method for unsupervised pre-training in Ball | What is the origin of the autoencoder neural networks?
According to the history provided in Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks (2015), auto-encoders were proposed as a method for unsupervised pre-training in Ballard, "Modular learning in neural networks," Proceedings AAAI (198... | What is the origin of the autoencoder neural networks?
According to the history provided in Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks (2015), auto-encoders were proposed as a method for unsupervised pre-training in Ball |
13,489 | What is the origin of the autoencoder neural networks? | The paper below talks about autoencoder indirectly and dates back to 1986.(which is a year earlier than the paper by Ballard in 1987)
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning internal representations by error propagation." , Parallel Distributed Processing. Vol 1: Foundations. MIT Press, Cambridge, MA,... | What is the origin of the autoencoder neural networks? | The paper below talks about autoencoder indirectly and dates back to 1986.(which is a year earlier than the paper by Ballard in 1987)
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning internal | What is the origin of the autoencoder neural networks?
The paper below talks about autoencoder indirectly and dates back to 1986.(which is a year earlier than the paper by Ballard in 1987)
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning internal representations by error propagation." , Parallel Distributed Pr... | What is the origin of the autoencoder neural networks?
The paper below talks about autoencoder indirectly and dates back to 1986.(which is a year earlier than the paper by Ballard in 1987)
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning internal |
13,490 | What is the origin of the autoencoder neural networks? | Reviving this thread - In "Neurocomputing" by Robert Hecht-Nielsen @ 1990 there is reference to a 1986 paper by Cottrell/Munro/Zipser that outlines use of a neural network that has the architecture of an autoencoder, and is trained on the identity function, for compression and reconstruction of image data. The term "a... | What is the origin of the autoencoder neural networks? | Reviving this thread - In "Neurocomputing" by Robert Hecht-Nielsen @ 1990 there is reference to a 1986 paper by Cottrell/Munro/Zipser that outlines use of a neural network that has the architecture of | What is the origin of the autoencoder neural networks?
Reviving this thread - In "Neurocomputing" by Robert Hecht-Nielsen @ 1990 there is reference to a 1986 paper by Cottrell/Munro/Zipser that outlines use of a neural network that has the architecture of an autoencoder, and is trained on the identity function, for com... | What is the origin of the autoencoder neural networks?
Reviving this thread - In "Neurocomputing" by Robert Hecht-Nielsen @ 1990 there is reference to a 1986 paper by Cottrell/Munro/Zipser that outlines use of a neural network that has the architecture of |
13,491 | What is the origin of the autoencoder neural networks? | The first clear autoencoder presentation featuring a feedforward, multilayer neural network with a bottleneck layer was presented by Kramer in 1991 (full text at https://people.engr.tamu.edu/rgutier/web_courses/cpsc636_s10/kramer1991nonlinearPCA.pdf). He discusses dimensionality reduction and feature extraction and app... | What is the origin of the autoencoder neural networks? | The first clear autoencoder presentation featuring a feedforward, multilayer neural network with a bottleneck layer was presented by Kramer in 1991 (full text at https://people.engr.tamu.edu/rgutier/w | What is the origin of the autoencoder neural networks?
The first clear autoencoder presentation featuring a feedforward, multilayer neural network with a bottleneck layer was presented by Kramer in 1991 (full text at https://people.engr.tamu.edu/rgutier/web_courses/cpsc636_s10/kramer1991nonlinearPCA.pdf). He discusses ... | What is the origin of the autoencoder neural networks?
The first clear autoencoder presentation featuring a feedforward, multilayer neural network with a bottleneck layer was presented by Kramer in 1991 (full text at https://people.engr.tamu.edu/rgutier/w |
13,492 | optimizing auc vs logloss in binary classification problems | As you mention, AUC is a rank statistic (i.e. scale invariant) & log loss is a calibration statistic. One may trivially construct a model which has the same AUC but fails to minimize log loss w.r.t. some other model by scaling the predicted values. Consider:
auc <- function(prediction, actual) {
mann_whit <- wilcox.... | optimizing auc vs logloss in binary classification problems | As you mention, AUC is a rank statistic (i.e. scale invariant) & log loss is a calibration statistic. One may trivially construct a model which has the same AUC but fails to minimize log loss w.r.t. s | optimizing auc vs logloss in binary classification problems
As you mention, AUC is a rank statistic (i.e. scale invariant) & log loss is a calibration statistic. One may trivially construct a model which has the same AUC but fails to minimize log loss w.r.t. some other model by scaling the predicted values. Consider:
a... | optimizing auc vs logloss in binary classification problems
As you mention, AUC is a rank statistic (i.e. scale invariant) & log loss is a calibration statistic. One may trivially construct a model which has the same AUC but fails to minimize log loss w.r.t. s |
13,493 | optimizing auc vs logloss in binary classification problems | For imbalanced labels, area under precision-recall curve is preferable to AUC (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349800/ or python scikit-learn docs)
Also, if your goal is to maximize precision, you can consider doing cross-validation to select the best model (algorithm + hyperparameters) using "precision" ... | optimizing auc vs logloss in binary classification problems | For imbalanced labels, area under precision-recall curve is preferable to AUC (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349800/ or python scikit-learn docs)
Also, if your goal is to maximize prec | optimizing auc vs logloss in binary classification problems
For imbalanced labels, area under precision-recall curve is preferable to AUC (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349800/ or python scikit-learn docs)
Also, if your goal is to maximize precision, you can consider doing cross-validation to select the... | optimizing auc vs logloss in binary classification problems
For imbalanced labels, area under precision-recall curve is preferable to AUC (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349800/ or python scikit-learn docs)
Also, if your goal is to maximize prec |
13,494 | What is the connection between regularization and the method of lagrange multipliers ? | Say we are optimizing a model with parameters $\vec{\theta}$, by minimizing some criterion $f(\vec{\theta})$ subject to a constraint on the magnitude of the parameter vector (for instance to implement a structural risk minimization approach by constructing a nested set of models of increasing complexity), we would need... | What is the connection between regularization and the method of lagrange multipliers ? | Say we are optimizing a model with parameters $\vec{\theta}$, by minimizing some criterion $f(\vec{\theta})$ subject to a constraint on the magnitude of the parameter vector (for instance to implement | What is the connection between regularization and the method of lagrange multipliers ?
Say we are optimizing a model with parameters $\vec{\theta}$, by minimizing some criterion $f(\vec{\theta})$ subject to a constraint on the magnitude of the parameter vector (for instance to implement a structural risk minimization a... | What is the connection between regularization and the method of lagrange multipliers ?
Say we are optimizing a model with parameters $\vec{\theta}$, by minimizing some criterion $f(\vec{\theta})$ subject to a constraint on the magnitude of the parameter vector (for instance to implement |
13,495 | How do CNN's avoid the vanishing gradient problem | The vanishing gradient problem requires us to use small learning rates with gradient descent which then needs many small steps to converge. This is a problem if you have a slow computer which takes a long time for each step. If you have a fast GPU which can perform many more steps in a day, this is less of a problem.
T... | How do CNN's avoid the vanishing gradient problem | The vanishing gradient problem requires us to use small learning rates with gradient descent which then needs many small steps to converge. This is a problem if you have a slow computer which takes a | How do CNN's avoid the vanishing gradient problem
The vanishing gradient problem requires us to use small learning rates with gradient descent which then needs many small steps to converge. This is a problem if you have a slow computer which takes a long time for each step. If you have a fast GPU which can perform many... | How do CNN's avoid the vanishing gradient problem
The vanishing gradient problem requires us to use small learning rates with gradient descent which then needs many small steps to converge. This is a problem if you have a slow computer which takes a |
13,496 | Do I need to drop variables that are correlated/collinear before running kmeans? | Don't drop any variables, but do consider using PCA. Here's why.
Firstly, as pointed out by Anony-mousse, k-means is not badly affected by collinearity/correlations. You don't need to throw away information because of that.
Secondly, if you drop your variables in the wrong way, you'll artificially bring some samples cl... | Do I need to drop variables that are correlated/collinear before running kmeans? | Don't drop any variables, but do consider using PCA. Here's why.
Firstly, as pointed out by Anony-mousse, k-means is not badly affected by collinearity/correlations. You don't need to throw away infor | Do I need to drop variables that are correlated/collinear before running kmeans?
Don't drop any variables, but do consider using PCA. Here's why.
Firstly, as pointed out by Anony-mousse, k-means is not badly affected by collinearity/correlations. You don't need to throw away information because of that.
Secondly, if yo... | Do I need to drop variables that are correlated/collinear before running kmeans?
Don't drop any variables, but do consider using PCA. Here's why.
Firstly, as pointed out by Anony-mousse, k-means is not badly affected by collinearity/correlations. You don't need to throw away infor |
13,497 | Do I need to drop variables that are correlated/collinear before running kmeans? | It's advisable to remove variables if they are highly correlated.
Irrespective of the clustering algorithm or linkage method, one thing that you generally follow is to find the distance between points. Keeping variables which are highly correlated is all but giving them more, double the weight in computing the distance... | Do I need to drop variables that are correlated/collinear before running kmeans? | It's advisable to remove variables if they are highly correlated.
Irrespective of the clustering algorithm or linkage method, one thing that you generally follow is to find the distance between points | Do I need to drop variables that are correlated/collinear before running kmeans?
It's advisable to remove variables if they are highly correlated.
Irrespective of the clustering algorithm or linkage method, one thing that you generally follow is to find the distance between points. Keeping variables which are highly co... | Do I need to drop variables that are correlated/collinear before running kmeans?
It's advisable to remove variables if they are highly correlated.
Irrespective of the clustering algorithm or linkage method, one thing that you generally follow is to find the distance between points |
13,498 | Do I need to drop variables that are correlated/collinear before running kmeans? | On a toy example in 2d or 3d, it shouldn't make much of a difference, it just adds some redundancy to your data: all your points are on an odd, (d-1) dimensional hyperplane. So are the cluster means. And distance in this (d-1) dimensional hyperplane is a linear multiple of the same distance, so it doesn't change anythi... | Do I need to drop variables that are correlated/collinear before running kmeans? | On a toy example in 2d or 3d, it shouldn't make much of a difference, it just adds some redundancy to your data: all your points are on an odd, (d-1) dimensional hyperplane. So are the cluster means. | Do I need to drop variables that are correlated/collinear before running kmeans?
On a toy example in 2d or 3d, it shouldn't make much of a difference, it just adds some redundancy to your data: all your points are on an odd, (d-1) dimensional hyperplane. So are the cluster means. And distance in this (d-1) dimensional ... | Do I need to drop variables that are correlated/collinear before running kmeans?
On a toy example in 2d or 3d, it shouldn't make much of a difference, it just adds some redundancy to your data: all your points are on an odd, (d-1) dimensional hyperplane. So are the cluster means. |
13,499 | Is building a multiclass classifier better than several binary ones? | First of all, you must ask yourself if your problem is multilabel (i.e. a single URL can belong to several classes) or not (i.e. a single URL can belong to only one class).
If you are in the former situation, go with a battery of binary classifiers, because this is a default way of doing multilabel problems.
If the lat... | Is building a multiclass classifier better than several binary ones? | First of all, you must ask yourself if your problem is multilabel (i.e. a single URL can belong to several classes) or not (i.e. a single URL can belong to only one class).
If you are in the former si | Is building a multiclass classifier better than several binary ones?
First of all, you must ask yourself if your problem is multilabel (i.e. a single URL can belong to several classes) or not (i.e. a single URL can belong to only one class).
If you are in the former situation, go with a battery of binary classifiers, b... | Is building a multiclass classifier better than several binary ones?
First of all, you must ask yourself if your problem is multilabel (i.e. a single URL can belong to several classes) or not (i.e. a single URL can belong to only one class).
If you are in the former si |
13,500 | Is building a multiclass classifier better than several binary ones? | This will depend on how your data is dispersed. There is a beautiful example that was given recently to a similar question where the OP wanted to know if a single linear discriminant function would be a better classifier for deciding population A vs B or C or one based on multiple linear discriminant functions that se... | Is building a multiclass classifier better than several binary ones? | This will depend on how your data is dispersed. There is a beautiful example that was given recently to a similar question where the OP wanted to know if a single linear discriminant function would b | Is building a multiclass classifier better than several binary ones?
This will depend on how your data is dispersed. There is a beautiful example that was given recently to a similar question where the OP wanted to know if a single linear discriminant function would be a better classifier for deciding population A vs ... | Is building a multiclass classifier better than several binary ones?
This will depend on how your data is dispersed. There is a beautiful example that was given recently to a similar question where the OP wanted to know if a single linear discriminant function would b |
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