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 |
|---|---|---|---|---|---|---|
50,701 | Prejudice in blind test | I do think it is a study design problem and a famous one in that some think RA Fisher did not actually realize it in making his famous Lady tasting cups of tea example and one that haunts clinical trials who try to prevent any unblinding of treatment assignment in clinical trials.
A solution suggested from what is don... | Prejudice in blind test | I do think it is a study design problem and a famous one in that some think RA Fisher did not actually realize it in making his famous Lady tasting cups of tea example and one that haunts clinical tri | Prejudice in blind test
I do think it is a study design problem and a famous one in that some think RA Fisher did not actually realize it in making his famous Lady tasting cups of tea example and one that haunts clinical trials who try to prevent any unblinding of treatment assignment in clinical trials.
A solution su... | Prejudice in blind test
I do think it is a study design problem and a famous one in that some think RA Fisher did not actually realize it in making his famous Lady tasting cups of tea example and one that haunts clinical tri |
50,702 | Prejudice in blind test | You could (truthfully) tell the participants that you will flip a coin each time you provide a soda and that the coin flip will determine P vs C. You can go on to explain to them, "If the last five were Coke (or Pepsi), Coke and Pepsi are equally likely on the next test." One problem is that some of your participants... | Prejudice in blind test | You could (truthfully) tell the participants that you will flip a coin each time you provide a soda and that the coin flip will determine P vs C. You can go on to explain to them, "If the last five w | Prejudice in blind test
You could (truthfully) tell the participants that you will flip a coin each time you provide a soda and that the coin flip will determine P vs C. You can go on to explain to them, "If the last five were Coke (or Pepsi), Coke and Pepsi are equally likely on the next test." One problem is that s... | Prejudice in blind test
You could (truthfully) tell the participants that you will flip a coin each time you provide a soda and that the coin flip will determine P vs C. You can go on to explain to them, "If the last five w |
50,703 | How do you detect if a given dataset has multivariate normal distribution? | By definition the random vector $X$ is multivariate normal if all linear combinations $a^T X$ have some (univariate) normal distribution. So one idea to test multivariate normality is to search among the vectors $a$ for one such that $a^T X$ is definitely not normal. That is the idea behind pp, projection pursuit met... | How do you detect if a given dataset has multivariate normal distribution? | By definition the random vector $X$ is multivariate normal if all linear combinations $a^T X$ have some (univariate) normal distribution. So one idea to test multivariate normality is to search among | How do you detect if a given dataset has multivariate normal distribution?
By definition the random vector $X$ is multivariate normal if all linear combinations $a^T X$ have some (univariate) normal distribution. So one idea to test multivariate normality is to search among the vectors $a$ for one such that $a^T X$ is... | How do you detect if a given dataset has multivariate normal distribution?
By definition the random vector $X$ is multivariate normal if all linear combinations $a^T X$ have some (univariate) normal distribution. So one idea to test multivariate normality is to search among |
50,704 | How do you detect if a given dataset has multivariate normal distribution? | A fast way of examining whether your data set is Gaussian distributed or not is to plot a histogram for each variable of your data set (if the dimensionality is small), or simply just calculate the sample skewness and kurtosis to check if they are Gaussian distributed. A Gaussian distributed data set will have skewness... | How do you detect if a given dataset has multivariate normal distribution? | A fast way of examining whether your data set is Gaussian distributed or not is to plot a histogram for each variable of your data set (if the dimensionality is small), or simply just calculate the sa | How do you detect if a given dataset has multivariate normal distribution?
A fast way of examining whether your data set is Gaussian distributed or not is to plot a histogram for each variable of your data set (if the dimensionality is small), or simply just calculate the sample skewness and kurtosis to check if they a... | How do you detect if a given dataset has multivariate normal distribution?
A fast way of examining whether your data set is Gaussian distributed or not is to plot a histogram for each variable of your data set (if the dimensionality is small), or simply just calculate the sa |
50,705 | nth moment, for 0 < n < 1 or n <0, do they exist? | Yes, investigated to at least some extent, as is readily seen by googling 'inverse moment' or 'fractional moments'.
Edit: In some cases these moments are rather straightforward to calculate. Here's an example of computing $E(X^{3/2})$ for $X\sim\text{gamma}(\alpha,1)$:
\begin{eqnarray}
E(X^{3/2}) &=& \int_0^\infty x^{3... | nth moment, for 0 < n < 1 or n <0, do they exist? | Yes, investigated to at least some extent, as is readily seen by googling 'inverse moment' or 'fractional moments'.
Edit: In some cases these moments are rather straightforward to calculate. Here's an | nth moment, for 0 < n < 1 or n <0, do they exist?
Yes, investigated to at least some extent, as is readily seen by googling 'inverse moment' or 'fractional moments'.
Edit: In some cases these moments are rather straightforward to calculate. Here's an example of computing $E(X^{3/2})$ for $X\sim\text{gamma}(\alpha,1)$:
... | nth moment, for 0 < n < 1 or n <0, do they exist?
Yes, investigated to at least some extent, as is readily seen by googling 'inverse moment' or 'fractional moments'.
Edit: In some cases these moments are rather straightforward to calculate. Here's an |
50,706 | Topic modeling, LDA and NMF | Note on implementing LDA for this problem: there are well-designed inference algorithms for huge numbers of documents. Specifically, you should check out "Online LDA", which can adaptively train the topics looking at small chunks of documents at a time.
Paper: http://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach20... | Topic modeling, LDA and NMF | Note on implementing LDA for this problem: there are well-designed inference algorithms for huge numbers of documents. Specifically, you should check out "Online LDA", which can adaptively train the | Topic modeling, LDA and NMF
Note on implementing LDA for this problem: there are well-designed inference algorithms for huge numbers of documents. Specifically, you should check out "Online LDA", which can adaptively train the topics looking at small chunks of documents at a time.
Paper: http://www.cs.princeton.edu/~b... | Topic modeling, LDA and NMF
Note on implementing LDA for this problem: there are well-designed inference algorithms for huge numbers of documents. Specifically, you should check out "Online LDA", which can adaptively train the |
50,707 | Inequality for bivariate normal distribution | The constants in this problem do not make much sense unless $X_1$ and $X_2$ have variance $1$ so that $X_1$ and $X_2-\mu_2$ are standard normal random variables, an assumption that the OP apparently is unwilling to make since this was asked about
in the comments, and the OP did not include the assumption in the revised... | Inequality for bivariate normal distribution | The constants in this problem do not make much sense unless $X_1$ and $X_2$ have variance $1$ so that $X_1$ and $X_2-\mu_2$ are standard normal random variables, an assumption that the OP apparently i | Inequality for bivariate normal distribution
The constants in this problem do not make much sense unless $X_1$ and $X_2$ have variance $1$ so that $X_1$ and $X_2-\mu_2$ are standard normal random variables, an assumption that the OP apparently is unwilling to make since this was asked about
in the comments, and the OP ... | Inequality for bivariate normal distribution
The constants in this problem do not make much sense unless $X_1$ and $X_2$ have variance $1$ so that $X_1$ and $X_2-\mu_2$ are standard normal random variables, an assumption that the OP apparently i |
50,708 | Validating a logistic regression for a specific $x$ | By its construction the logistic regression models predicts probabilities,
$$\hat P(Y =1 \mid X=x_0) = \frac{1}{1+\exp{\{\hat{\alpha} + \hat{\beta} x_0\}}}$$
The proportion of $Y=1$ in the test sample of size $n$, with all $x$'s equal, is a different estimator of the same conditional probability, denote it $\hat p_{1|x... | Validating a logistic regression for a specific $x$ | By its construction the logistic regression models predicts probabilities,
$$\hat P(Y =1 \mid X=x_0) = \frac{1}{1+\exp{\{\hat{\alpha} + \hat{\beta} x_0\}}}$$
The proportion of $Y=1$ in the test sample | Validating a logistic regression for a specific $x$
By its construction the logistic regression models predicts probabilities,
$$\hat P(Y =1 \mid X=x_0) = \frac{1}{1+\exp{\{\hat{\alpha} + \hat{\beta} x_0\}}}$$
The proportion of $Y=1$ in the test sample of size $n$, with all $x$'s equal, is a different estimator of the ... | Validating a logistic regression for a specific $x$
By its construction the logistic regression models predicts probabilities,
$$\hat P(Y =1 \mid X=x_0) = \frac{1}{1+\exp{\{\hat{\alpha} + \hat{\beta} x_0\}}}$$
The proportion of $Y=1$ in the test sample |
50,709 | Hierarchical decomposition of an imbalanced multiclass classification problem | Hierarchical classification models frequently fail for different reasons. This is why flat classification methods based on one-vs-rest are usually preferred.
One of the main reasons discussed in the literature is that once an error is made in the upper levels of the hierarchy the model has no way to recover. To analyz... | Hierarchical decomposition of an imbalanced multiclass classification problem | Hierarchical classification models frequently fail for different reasons. This is why flat classification methods based on one-vs-rest are usually preferred.
One of the main reasons discussed in the | Hierarchical decomposition of an imbalanced multiclass classification problem
Hierarchical classification models frequently fail for different reasons. This is why flat classification methods based on one-vs-rest are usually preferred.
One of the main reasons discussed in the literature is that once an error is made i... | Hierarchical decomposition of an imbalanced multiclass classification problem
Hierarchical classification models frequently fail for different reasons. This is why flat classification methods based on one-vs-rest are usually preferred.
One of the main reasons discussed in the |
50,710 | Probability of independent events within a specified window | If the days are truly independent as you say then let us denote an event A as X events in any Y day period and denote P(A), the probability of X events in Y days:
P(A) = COMBIN(Y,X) * (0.01 ^ X) * [0.99 ^ (Y - X)]
This is true for any Y day window provided the probability of an event occurring is not time dependent.
Th... | Probability of independent events within a specified window | If the days are truly independent as you say then let us denote an event A as X events in any Y day period and denote P(A), the probability of X events in Y days:
P(A) = COMBIN(Y,X) * (0.01 ^ X) * [0. | Probability of independent events within a specified window
If the days are truly independent as you say then let us denote an event A as X events in any Y day period and denote P(A), the probability of X events in Y days:
P(A) = COMBIN(Y,X) * (0.01 ^ X) * [0.99 ^ (Y - X)]
This is true for any Y day window provided the... | Probability of independent events within a specified window
If the days are truly independent as you say then let us denote an event A as X events in any Y day period and denote P(A), the probability of X events in Y days:
P(A) = COMBIN(Y,X) * (0.01 ^ X) * [0. |
50,711 | Probability of independent events within a specified window | Here is some code, a brute-force approach, to get a ballpark for the probability.
In this I assume that the particular 30 day window does not matter. If there were 3 at the end of one month and 2 at the beginning of the next, I still count it as 5 in a row.
set.seed(1)
#number of runs
N <- 5e5
#max number of events p... | Probability of independent events within a specified window | Here is some code, a brute-force approach, to get a ballpark for the probability.
In this I assume that the particular 30 day window does not matter. If there were 3 at the end of one month and 2 at t | Probability of independent events within a specified window
Here is some code, a brute-force approach, to get a ballpark for the probability.
In this I assume that the particular 30 day window does not matter. If there were 3 at the end of one month and 2 at the beginning of the next, I still count it as 5 in a row.
se... | Probability of independent events within a specified window
Here is some code, a brute-force approach, to get a ballpark for the probability.
In this I assume that the particular 30 day window does not matter. If there were 3 at the end of one month and 2 at t |
50,712 | Calculate probability for LibLinear classification results | You can use a sigmoid function $f(d) = \frac{1}{1 + e^{-\alpha(d-\beta)}}$
to convert your SVM decision value $d = (w, x) + b$ into a number between 0 and 1 which can be treated as probability. You can adjust parameters $\alpha$ and $\beta$ depending on your data.
For more elaborate approaches, see these papers:
B.Zad... | Calculate probability for LibLinear classification results | You can use a sigmoid function $f(d) = \frac{1}{1 + e^{-\alpha(d-\beta)}}$
to convert your SVM decision value $d = (w, x) + b$ into a number between 0 and 1 which can be treated as probability. You ca | Calculate probability for LibLinear classification results
You can use a sigmoid function $f(d) = \frac{1}{1 + e^{-\alpha(d-\beta)}}$
to convert your SVM decision value $d = (w, x) + b$ into a number between 0 and 1 which can be treated as probability. You can adjust parameters $\alpha$ and $\beta$ depending on your da... | Calculate probability for LibLinear classification results
You can use a sigmoid function $f(d) = \frac{1}{1 + e^{-\alpha(d-\beta)}}$
to convert your SVM decision value $d = (w, x) + b$ into a number between 0 and 1 which can be treated as probability. You ca |
50,713 | Calculate probability for LibLinear classification results | At least in R, only two algorithms provide the probabilities in LiblineaR interface.
Here is the FAQ of the actual library:
Q: How do I choose the solver? Should I use logistic regression or linear SVM? How about L1/L2 regularization?
Generally we recommend linear SVM as its training is faster and the accuracy is compe... | Calculate probability for LibLinear classification results | At least in R, only two algorithms provide the probabilities in LiblineaR interface.
Here is the FAQ of the actual library:
Q: How do I choose the solver? Should I use logistic regression or linear SV | Calculate probability for LibLinear classification results
At least in R, only two algorithms provide the probabilities in LiblineaR interface.
Here is the FAQ of the actual library:
Q: How do I choose the solver? Should I use logistic regression or linear SVM? How about L1/L2 regularization?
Generally we recommend lin... | Calculate probability for LibLinear classification results
At least in R, only two algorithms provide the probabilities in LiblineaR interface.
Here is the FAQ of the actual library:
Q: How do I choose the solver? Should I use logistic regression or linear SV |
50,714 | How to determine a user's favorite content producer from individual ratings? | Your question indicates that you want a score that gives some weight both to watching a film (whether the user likes it or not) and some additional weight to liking it. I would start by defining $M_{ud}$ as the maximum possible number of films by director $d$ watched by user $u$ as a proportion of all films watched by ... | How to determine a user's favorite content producer from individual ratings? | Your question indicates that you want a score that gives some weight both to watching a film (whether the user likes it or not) and some additional weight to liking it. I would start by defining $M_{u | How to determine a user's favorite content producer from individual ratings?
Your question indicates that you want a score that gives some weight both to watching a film (whether the user likes it or not) and some additional weight to liking it. I would start by defining $M_{ud}$ as the maximum possible number of films... | How to determine a user's favorite content producer from individual ratings?
Your question indicates that you want a score that gives some weight both to watching a film (whether the user likes it or not) and some additional weight to liking it. I would start by defining $M_{u |
50,715 | How to determine a user's favorite content producer from individual ratings? | A really simple answer is the modal director, but that does not adjust for the composition, since some directors may be more prolific or simply older.
For each user, I would consider the ratio of liked movies by director $i$ to all movies watched by director $i$, scaled by the ratio of all movies watched to all movies ... | How to determine a user's favorite content producer from individual ratings? | A really simple answer is the modal director, but that does not adjust for the composition, since some directors may be more prolific or simply older.
For each user, I would consider the ratio of like | How to determine a user's favorite content producer from individual ratings?
A really simple answer is the modal director, but that does not adjust for the composition, since some directors may be more prolific or simply older.
For each user, I would consider the ratio of liked movies by director $i$ to all movies watc... | How to determine a user's favorite content producer from individual ratings?
A really simple answer is the modal director, but that does not adjust for the composition, since some directors may be more prolific or simply older.
For each user, I would consider the ratio of like |
50,716 | How to determine a user's favorite content producer from individual ratings? | I think that some kind of recommender system might be what you are looking for. | How to determine a user's favorite content producer from individual ratings? | I think that some kind of recommender system might be what you are looking for. | How to determine a user's favorite content producer from individual ratings?
I think that some kind of recommender system might be what you are looking for. | How to determine a user's favorite content producer from individual ratings?
I think that some kind of recommender system might be what you are looking for. |
50,717 | Should you use normalized or non-normalized data to develope your model? | The difference between using normalized and nonnormalized data is one of interpretation. If you use the original data, the coefficients apply to changes of one unit on the original scale. If you use the normalized data, they apply to changes of one unit on the new scale (usually, one standard deviation).
This is an iss... | Should you use normalized or non-normalized data to develope your model? | The difference between using normalized and nonnormalized data is one of interpretation. If you use the original data, the coefficients apply to changes of one unit on the original scale. If you use t | Should you use normalized or non-normalized data to develope your model?
The difference between using normalized and nonnormalized data is one of interpretation. If you use the original data, the coefficients apply to changes of one unit on the original scale. If you use the normalized data, they apply to changes of on... | Should you use normalized or non-normalized data to develope your model?
The difference between using normalized and nonnormalized data is one of interpretation. If you use the original data, the coefficients apply to changes of one unit on the original scale. If you use t |
50,718 | Determining smoothing parameter in HP filter for hourly data | The equation you are looking for is
$$\lambda_\alpha = \frac{1}{\alpha^4}\lambda_1$$
which is the adjustment factor derived by Ravn and Uhlig (2002). They derived the smoothing factor for annual data with this formula using the $\lambda = 1600$ for monthly data which was originally suggested by Hodrick and Prescott. Th... | Determining smoothing parameter in HP filter for hourly data | The equation you are looking for is
$$\lambda_\alpha = \frac{1}{\alpha^4}\lambda_1$$
which is the adjustment factor derived by Ravn and Uhlig (2002). They derived the smoothing factor for annual data | Determining smoothing parameter in HP filter for hourly data
The equation you are looking for is
$$\lambda_\alpha = \frac{1}{\alpha^4}\lambda_1$$
which is the adjustment factor derived by Ravn and Uhlig (2002). They derived the smoothing factor for annual data with this formula using the $\lambda = 1600$ for monthly da... | Determining smoothing parameter in HP filter for hourly data
The equation you are looking for is
$$\lambda_\alpha = \frac{1}{\alpha^4}\lambda_1$$
which is the adjustment factor derived by Ravn and Uhlig (2002). They derived the smoothing factor for annual data |
50,719 | How to control for market return in an (SPSS) OLS? | It'd be helpful if you told us what procedure you used. My answers rely on some guesses.
Question 1: If you're running the OLS regression using Analyze > Regression, then they cannot be random effects because this module does not allows it. So, they can be seen as fixed effects. If you have used Mixed module then it wo... | How to control for market return in an (SPSS) OLS? | It'd be helpful if you told us what procedure you used. My answers rely on some guesses.
Question 1: If you're running the OLS regression using Analyze > Regression, then they cannot be random effects | How to control for market return in an (SPSS) OLS?
It'd be helpful if you told us what procedure you used. My answers rely on some guesses.
Question 1: If you're running the OLS regression using Analyze > Regression, then they cannot be random effects because this module does not allows it. So, they can be seen as fixe... | How to control for market return in an (SPSS) OLS?
It'd be helpful if you told us what procedure you used. My answers rely on some guesses.
Question 1: If you're running the OLS regression using Analyze > Regression, then they cannot be random effects |
50,720 | How to control for market return in an (SPSS) OLS? | For Q1: since weekDay and your dummy variables are not coming from random causes, I think they can be considered as fixed effects. | How to control for market return in an (SPSS) OLS? | For Q1: since weekDay and your dummy variables are not coming from random causes, I think they can be considered as fixed effects. | How to control for market return in an (SPSS) OLS?
For Q1: since weekDay and your dummy variables are not coming from random causes, I think they can be considered as fixed effects. | How to control for market return in an (SPSS) OLS?
For Q1: since weekDay and your dummy variables are not coming from random causes, I think they can be considered as fixed effects. |
50,721 | How to control for market return in an (SPSS) OLS? | Treating the market return as additive by just including it as a regressor might not be the best approach. You might consider using the stockRet / marketRet as the dependent variable, which gives you a proportionality model. | How to control for market return in an (SPSS) OLS? | Treating the market return as additive by just including it as a regressor might not be the best approach. You might consider using the stockRet / marketRet as the dependent variable, which gives you | How to control for market return in an (SPSS) OLS?
Treating the market return as additive by just including it as a regressor might not be the best approach. You might consider using the stockRet / marketRet as the dependent variable, which gives you a proportionality model. | How to control for market return in an (SPSS) OLS?
Treating the market return as additive by just including it as a regressor might not be the best approach. You might consider using the stockRet / marketRet as the dependent variable, which gives you |
50,722 | Custom power analysis in R | If one assumes $d = \frac{(\hat N_2-\hat N_1)}{\hat N_2}$, which is the percent differnce. Then:
$$
Z = \frac{d}{\sqrt{2}*cv(\hat N)}
$$
$$
Z = \frac{\frac{(\hat N_2-\hat N_1)}{\hat N_2}}{\sqrt{2}*cv(\hat N)}
$$
$$
Z = \frac{(\hat N_2-\hat N_1)}{\sqrt{2}*cv(\hat N)*\hat N_2}
$$
$$
Z = \frac{(\hat N_2-\hat N_1)}{\sqrt{... | Custom power analysis in R | If one assumes $d = \frac{(\hat N_2-\hat N_1)}{\hat N_2}$, which is the percent differnce. Then:
$$
Z = \frac{d}{\sqrt{2}*cv(\hat N)}
$$
$$
Z = \frac{\frac{(\hat N_2-\hat N_1)}{\hat N_2}}{\sqrt{2}*cv | Custom power analysis in R
If one assumes $d = \frac{(\hat N_2-\hat N_1)}{\hat N_2}$, which is the percent differnce. Then:
$$
Z = \frac{d}{\sqrt{2}*cv(\hat N)}
$$
$$
Z = \frac{\frac{(\hat N_2-\hat N_1)}{\hat N_2}}{\sqrt{2}*cv(\hat N)}
$$
$$
Z = \frac{(\hat N_2-\hat N_1)}{\sqrt{2}*cv(\hat N)*\hat N_2}
$$
$$
Z = \frac{... | Custom power analysis in R
If one assumes $d = \frac{(\hat N_2-\hat N_1)}{\hat N_2}$, which is the percent differnce. Then:
$$
Z = \frac{d}{\sqrt{2}*cv(\hat N)}
$$
$$
Z = \frac{\frac{(\hat N_2-\hat N_1)}{\hat N_2}}{\sqrt{2}*cv |
50,723 | How to write a poker player using Bayes networks | From the book you mention:
Note that the existing structure makes the assumption that the opponent's action depends
only on its current hand.
And a little bit further:
There are four action probability tables $P_i(OPP\_Action|OPP\_Current)$, corresponding to the four rounds of betting. These report the conditional... | How to write a poker player using Bayes networks | From the book you mention:
Note that the existing structure makes the assumption that the opponent's action depends
only on its current hand.
And a little bit further:
There are four action proba | How to write a poker player using Bayes networks
From the book you mention:
Note that the existing structure makes the assumption that the opponent's action depends
only on its current hand.
And a little bit further:
There are four action probability tables $P_i(OPP\_Action|OPP\_Current)$, corresponding to the fou... | How to write a poker player using Bayes networks
From the book you mention:
Note that the existing structure makes the assumption that the opponent's action depends
only on its current hand.
And a little bit further:
There are four action proba |
50,724 | Panel-data exploratory data analysis | I always start by doing a PCA (Principal Component Analysis) in R because it takes almost no writing. Say you have all this in a data.frame that we call data.
pca <- prcomp(data)
# Screeplot.
plot(pca)
# Biplot.
biplot(pca)
For R users, there is also the ggplot2 library. I know that it can do wonders for data represen... | Panel-data exploratory data analysis | I always start by doing a PCA (Principal Component Analysis) in R because it takes almost no writing. Say you have all this in a data.frame that we call data.
pca <- prcomp(data)
# Screeplot.
plot(pca | Panel-data exploratory data analysis
I always start by doing a PCA (Principal Component Analysis) in R because it takes almost no writing. Say you have all this in a data.frame that we call data.
pca <- prcomp(data)
# Screeplot.
plot(pca)
# Biplot.
biplot(pca)
For R users, there is also the ggplot2 library. I know tha... | Panel-data exploratory data analysis
I always start by doing a PCA (Principal Component Analysis) in R because it takes almost no writing. Say you have all this in a data.frame that we call data.
pca <- prcomp(data)
# Screeplot.
plot(pca |
50,725 | Panel-data exploratory data analysis | It's not clear to me what you've graphed when you say "scatter plots between sales, R&D, and advertising". For example, have you done something like:
library (lattice)
xyplot (sale ~ xrd | year, groups=sicagg)
xyplot (sale ~ xrd | sicagg, groups=year)
Not sure what sicagg is; I assume it's a factor variable in my exam... | Panel-data exploratory data analysis | It's not clear to me what you've graphed when you say "scatter plots between sales, R&D, and advertising". For example, have you done something like:
library (lattice)
xyplot (sale ~ xrd | year, group | Panel-data exploratory data analysis
It's not clear to me what you've graphed when you say "scatter plots between sales, R&D, and advertising". For example, have you done something like:
library (lattice)
xyplot (sale ~ xrd | year, groups=sicagg)
xyplot (sale ~ xrd | sicagg, groups=year)
Not sure what sicagg is; I ass... | Panel-data exploratory data analysis
It's not clear to me what you've graphed when you say "scatter plots between sales, R&D, and advertising". For example, have you done something like:
library (lattice)
xyplot (sale ~ xrd | year, group |
50,726 | Weighted clustering algorithm | You're really given a planar graph and you want to find connected components that have the smallest "spread" in values. While I don't know how to get an answer with provably guarantees, the following heuristic might work well.
Assume all states have weights between 0 and $2^k$ say (for some $k$). Label all states with... | Weighted clustering algorithm | You're really given a planar graph and you want to find connected components that have the smallest "spread" in values. While I don't know how to get an answer with provably guarantees, the following | Weighted clustering algorithm
You're really given a planar graph and you want to find connected components that have the smallest "spread" in values. While I don't know how to get an answer with provably guarantees, the following heuristic might work well.
Assume all states have weights between 0 and $2^k$ say (for so... | Weighted clustering algorithm
You're really given a planar graph and you want to find connected components that have the smallest "spread" in values. While I don't know how to get an answer with provably guarantees, the following |
50,727 | Weighted clustering algorithm | This looks like a standard variation of bin packing problem with constraints to me.
https://en.wikipedia.org/wiki/Bin_packing_problem
It does not so much like clustering to me: the distances seems to be solely a constraint that only adjacent states must be selected. So none of the stuff you find under the term of "clus... | Weighted clustering algorithm | This looks like a standard variation of bin packing problem with constraints to me.
https://en.wikipedia.org/wiki/Bin_packing_problem
It does not so much like clustering to me: the distances seems to | Weighted clustering algorithm
This looks like a standard variation of bin packing problem with constraints to me.
https://en.wikipedia.org/wiki/Bin_packing_problem
It does not so much like clustering to me: the distances seems to be solely a constraint that only adjacent states must be selected. So none of the stuff yo... | Weighted clustering algorithm
This looks like a standard variation of bin packing problem with constraints to me.
https://en.wikipedia.org/wiki/Bin_packing_problem
It does not so much like clustering to me: the distances seems to |
50,728 | Weighted clustering algorithm | What about using Graph Partition (http://en.wikipedia.org/wiki/Graph_partition)?
Where the graph here would be the USA, where the nodes are the states, the edges are the connections between states (i.e. there is an edge between two states if they are adjacent to each other). The subgraphs, or partitions would be the t... | Weighted clustering algorithm | What about using Graph Partition (http://en.wikipedia.org/wiki/Graph_partition)?
Where the graph here would be the USA, where the nodes are the states, the edges are the connections between states (i | Weighted clustering algorithm
What about using Graph Partition (http://en.wikipedia.org/wiki/Graph_partition)?
Where the graph here would be the USA, where the nodes are the states, the edges are the connections between states (i.e. there is an edge between two states if they are adjacent to each other). The subgraphs... | Weighted clustering algorithm
What about using Graph Partition (http://en.wikipedia.org/wiki/Graph_partition)?
Where the graph here would be the USA, where the nodes are the states, the edges are the connections between states (i |
50,729 | $\sigma$-algebra intersection of infinite subsets | We have for each positive integer $n$ that $a-1/n \lt a\lt b\lt b+1/n$, hence $[a,b]\subset (a-1/n,b+1/n)$; in particular $[a,b]$ is contained in the intersection. If $x\in (a-1/n,b+1/n)$ for each positive $n$, then $a-1/n\lt x\lt b+1/n$. Taking the limit $n\to \infty$, we get $x\in [a,b]$.
Let $\mathcal A$ be a $\sigm... | $\sigma$-algebra intersection of infinite subsets | We have for each positive integer $n$ that $a-1/n \lt a\lt b\lt b+1/n$, hence $[a,b]\subset (a-1/n,b+1/n)$; in particular $[a,b]$ is contained in the intersection. If $x\in (a-1/n,b+1/n)$ for each pos | $\sigma$-algebra intersection of infinite subsets
We have for each positive integer $n$ that $a-1/n \lt a\lt b\lt b+1/n$, hence $[a,b]\subset (a-1/n,b+1/n)$; in particular $[a,b]$ is contained in the intersection. If $x\in (a-1/n,b+1/n)$ for each positive $n$, then $a-1/n\lt x\lt b+1/n$. Taking the limit $n\to \infty$,... | $\sigma$-algebra intersection of infinite subsets
We have for each positive integer $n$ that $a-1/n \lt a\lt b\lt b+1/n$, hence $[a,b]\subset (a-1/n,b+1/n)$; in particular $[a,b]$ is contained in the intersection. If $x\in (a-1/n,b+1/n)$ for each pos |
50,730 | How do I handle measurement error in sparse data? | Your problem has actually got two main parts.
The first is related to the statistics. You will need to assess the data in light of your knowledge of the system and the option to match different distributions to confirm the kind of data you have is a good first step. Once you have a good model you can then begin to ma... | How do I handle measurement error in sparse data? | Your problem has actually got two main parts.
The first is related to the statistics. You will need to assess the data in light of your knowledge of the system and the option to match different distr | How do I handle measurement error in sparse data?
Your problem has actually got two main parts.
The first is related to the statistics. You will need to assess the data in light of your knowledge of the system and the option to match different distributions to confirm the kind of data you have is a good first step. O... | How do I handle measurement error in sparse data?
Your problem has actually got two main parts.
The first is related to the statistics. You will need to assess the data in light of your knowledge of the system and the option to match different distr |
50,731 | How do I handle measurement error in sparse data? | As mentioned in the comments, the question is a bit vague so it is hard to make sure I actually answer it.
If your property X is the mean of twenty measurements, then you can compute a standard deviation from that sample, say σ. If you believe that measurements are independent, the standard deviation of X is σ / √20.
T... | How do I handle measurement error in sparse data? | As mentioned in the comments, the question is a bit vague so it is hard to make sure I actually answer it.
If your property X is the mean of twenty measurements, then you can compute a standard deviat | How do I handle measurement error in sparse data?
As mentioned in the comments, the question is a bit vague so it is hard to make sure I actually answer it.
If your property X is the mean of twenty measurements, then you can compute a standard deviation from that sample, say σ. If you believe that measurements are inde... | How do I handle measurement error in sparse data?
As mentioned in the comments, the question is a bit vague so it is hard to make sure I actually answer it.
If your property X is the mean of twenty measurements, then you can compute a standard deviat |
50,732 | Average of a tail of a normal distribution | Cyan offered a link that answers the question but since the question remains without an actual answer, I'll put one in.
While not strictly "closed form" by the usual definitions, I expect people doing statistical work will mostly want to admit the normal cdf (or the error function, which would serve the same purpose) ... | Average of a tail of a normal distribution | Cyan offered a link that answers the question but since the question remains without an actual answer, I'll put one in.
While not strictly "closed form" by the usual definitions, I expect people doin | Average of a tail of a normal distribution
Cyan offered a link that answers the question but since the question remains without an actual answer, I'll put one in.
While not strictly "closed form" by the usual definitions, I expect people doing statistical work will mostly want to admit the normal cdf (or the error fun... | Average of a tail of a normal distribution
Cyan offered a link that answers the question but since the question remains without an actual answer, I'll put one in.
While not strictly "closed form" by the usual definitions, I expect people doin |
50,733 | Time-wise treatment effect / survival analysis | I think you're going to struggle with any sort of definitive treatment effect, because you lack a comparison group of any sort. You need not necessarily have a control group - there's plenty of methods in the case-crossover literature for using cases as their own controls during unexposed time periods, but if everyone ... | Time-wise treatment effect / survival analysis | I think you're going to struggle with any sort of definitive treatment effect, because you lack a comparison group of any sort. You need not necessarily have a control group - there's plenty of method | Time-wise treatment effect / survival analysis
I think you're going to struggle with any sort of definitive treatment effect, because you lack a comparison group of any sort. You need not necessarily have a control group - there's plenty of methods in the case-crossover literature for using cases as their own controls ... | Time-wise treatment effect / survival analysis
I think you're going to struggle with any sort of definitive treatment effect, because you lack a comparison group of any sort. You need not necessarily have a control group - there's plenty of method |
50,734 | Life after the Box-Cox transformation | First of all, if you mean a linear regression model, it does not assume the data are normally distributed, it assumes the error as estimated by the residuals is normally distributed (in fact, they should be iid $\mathcal{N}(0,\sigma)$).
Second, if that assumption is violated and you want to keep your original units,... | Life after the Box-Cox transformation | First of all, if you mean a linear regression model, it does not assume the data are normally distributed, it assumes the error as estimated by the residuals is normally distributed (in fact, they sh | Life after the Box-Cox transformation
First of all, if you mean a linear regression model, it does not assume the data are normally distributed, it assumes the error as estimated by the residuals is normally distributed (in fact, they should be iid $\mathcal{N}(0,\sigma)$).
Second, if that assumption is violated and... | Life after the Box-Cox transformation
First of all, if you mean a linear regression model, it does not assume the data are normally distributed, it assumes the error as estimated by the residuals is normally distributed (in fact, they sh |
50,735 | Life after the Box-Cox transformation | It sounds like your model is of this form; $$Y_i|x_i = f(x_i, \beta) + \epsilon_i,$$ where $Y_i$ denotes the $i$th measured outcome, $x_i$ is a vector of covariates for that outcome (i.e. experimental circumstances), which with (unknown) parameters $\beta$ determines the expected value $f(x_i, \beta)$ for that observat... | Life after the Box-Cox transformation | It sounds like your model is of this form; $$Y_i|x_i = f(x_i, \beta) + \epsilon_i,$$ where $Y_i$ denotes the $i$th measured outcome, $x_i$ is a vector of covariates for that outcome (i.e. experimental | Life after the Box-Cox transformation
It sounds like your model is of this form; $$Y_i|x_i = f(x_i, \beta) + \epsilon_i,$$ where $Y_i$ denotes the $i$th measured outcome, $x_i$ is a vector of covariates for that outcome (i.e. experimental circumstances), which with (unknown) parameters $\beta$ determines the expected v... | Life after the Box-Cox transformation
It sounds like your model is of this form; $$Y_i|x_i = f(x_i, \beta) + \epsilon_i,$$ where $Y_i$ denotes the $i$th measured outcome, $x_i$ is a vector of covariates for that outcome (i.e. experimental |
50,736 | Multiple regression with constraints on coefficients [closed] | I've used the MGCV package to fit a constrained regression where the coefficients could not be negative:
Constrained Regression
Also the 'quadprog' package with the solve.QP function may be useful.
Both, however, have a little bit of a learning curve, at least for me. | Multiple regression with constraints on coefficients [closed] | I've used the MGCV package to fit a constrained regression where the coefficients could not be negative:
Constrained Regression
Also the 'quadprog' package with the solve.QP function may be useful.
B | Multiple regression with constraints on coefficients [closed]
I've used the MGCV package to fit a constrained regression where the coefficients could not be negative:
Constrained Regression
Also the 'quadprog' package with the solve.QP function may be useful.
Both, however, have a little bit of a learning curve, at le... | Multiple regression with constraints on coefficients [closed]
I've used the MGCV package to fit a constrained regression where the coefficients could not be negative:
Constrained Regression
Also the 'quadprog' package with the solve.QP function may be useful.
B |
50,737 | Multiple regression with constraints on coefficients [closed] | You can use ConsReg package.
cran.r-project.org/web/packages/ConsReg/index.html
It's very easy to use | Multiple regression with constraints on coefficients [closed] | You can use ConsReg package.
cran.r-project.org/web/packages/ConsReg/index.html
It's very easy to use | Multiple regression with constraints on coefficients [closed]
You can use ConsReg package.
cran.r-project.org/web/packages/ConsReg/index.html
It's very easy to use | Multiple regression with constraints on coefficients [closed]
You can use ConsReg package.
cran.r-project.org/web/packages/ConsReg/index.html
It's very easy to use |
50,738 | Segmentation of employees | The best approach seems to be using Bayesian networks, which are used for just that purpose. Here's a free tool for automating the process.
Depending on how much effort you're willing to invest, you can go all the way to causal analysis and intervention calculus, which are the natural next step. | Segmentation of employees | The best approach seems to be using Bayesian networks, which are used for just that purpose. Here's a free tool for automating the process.
Depending on how much effort you're willing to invest, you c | Segmentation of employees
The best approach seems to be using Bayesian networks, which are used for just that purpose. Here's a free tool for automating the process.
Depending on how much effort you're willing to invest, you can go all the way to causal analysis and intervention calculus, which are the natural next ste... | Segmentation of employees
The best approach seems to be using Bayesian networks, which are used for just that purpose. Here's a free tool for automating the process.
Depending on how much effort you're willing to invest, you c |
50,739 | How to calculate threshold level for mutual information scores? | You could try shuffling your data to make it independent, and use the same procedure to compute the MI score. This would provide a surrogate for the null hypothesis, and if you are okay with p-values, perhaps you can choose a threshold by selecting something like p-value of 0.05. | How to calculate threshold level for mutual information scores? | You could try shuffling your data to make it independent, and use the same procedure to compute the MI score. This would provide a surrogate for the null hypothesis, and if you are okay with p-values, | How to calculate threshold level for mutual information scores?
You could try shuffling your data to make it independent, and use the same procedure to compute the MI score. This would provide a surrogate for the null hypothesis, and if you are okay with p-values, perhaps you can choose a threshold by selecting somethi... | How to calculate threshold level for mutual information scores?
You could try shuffling your data to make it independent, and use the same procedure to compute the MI score. This would provide a surrogate for the null hypothesis, and if you are okay with p-values, |
50,740 | How to calculate threshold level for mutual information scores? | Computing Normalized Mutual Information will put the values into more meaningful terms (NMI = 0, two variables contain no information about one another, NMI = 1, two variables contain perfect information about one another).
To determine a threshold I think it will really depends on what you plan to do after you state... | How to calculate threshold level for mutual information scores? | Computing Normalized Mutual Information will put the values into more meaningful terms (NMI = 0, two variables contain no information about one another, NMI = 1, two variables contain perfect informat | How to calculate threshold level for mutual information scores?
Computing Normalized Mutual Information will put the values into more meaningful terms (NMI = 0, two variables contain no information about one another, NMI = 1, two variables contain perfect information about one another).
To determine a threshold I thi... | How to calculate threshold level for mutual information scores?
Computing Normalized Mutual Information will put the values into more meaningful terms (NMI = 0, two variables contain no information about one another, NMI = 1, two variables contain perfect informat |
50,741 | What is conditioning in spatial statistics? | The answer is clearly "yes". Your resulting pattern at the end of step 5 is conditioned on the points in the top corner. Imagine doing steps 3,4 and 5 again. You'll get the same points in the top left corner, and different points elsewhere.
There's also the element of working out how you've generated the new points gi... | What is conditioning in spatial statistics? | The answer is clearly "yes". Your resulting pattern at the end of step 5 is conditioned on the points in the top corner. Imagine doing steps 3,4 and 5 again. You'll get the same points in the top left | What is conditioning in spatial statistics?
The answer is clearly "yes". Your resulting pattern at the end of step 5 is conditioned on the points in the top corner. Imagine doing steps 3,4 and 5 again. You'll get the same points in the top left corner, and different points elsewhere.
There's also the element of workin... | What is conditioning in spatial statistics?
The answer is clearly "yes". Your resulting pattern at the end of step 5 is conditioned on the points in the top corner. Imagine doing steps 3,4 and 5 again. You'll get the same points in the top left |
50,742 | How to determine the combination of factor levels for which the response variable is highest | This question is amenable to a decision tree analysis technique. With a continuous outcome, the software will simply put cut-points in the middle of measured values, so the cuts will fall between levels you have measured, rather than being actual levels. The categorical predictors work well with this method, as you'll ... | How to determine the combination of factor levels for which the response variable is highest | This question is amenable to a decision tree analysis technique. With a continuous outcome, the software will simply put cut-points in the middle of measured values, so the cuts will fall between leve | How to determine the combination of factor levels for which the response variable is highest
This question is amenable to a decision tree analysis technique. With a continuous outcome, the software will simply put cut-points in the middle of measured values, so the cuts will fall between levels you have measured, rathe... | How to determine the combination of factor levels for which the response variable is highest
This question is amenable to a decision tree analysis technique. With a continuous outcome, the software will simply put cut-points in the middle of measured values, so the cuts will fall between leve |
50,743 | Combining ratings from multiple raters of different accuracy | If the poorer raters are that bad, it suggests they are not adding information and could be dropped from the pool of raters. This would be preferable to weighting their ratings because:
sometimes their "5"s will really be "5"s according to your better raters. Given that your better raters are providing all the informa... | Combining ratings from multiple raters of different accuracy | If the poorer raters are that bad, it suggests they are not adding information and could be dropped from the pool of raters. This would be preferable to weighting their ratings because:
sometimes the | Combining ratings from multiple raters of different accuracy
If the poorer raters are that bad, it suggests they are not adding information and could be dropped from the pool of raters. This would be preferable to weighting their ratings because:
sometimes their "5"s will really be "5"s according to your better raters... | Combining ratings from multiple raters of different accuracy
If the poorer raters are that bad, it suggests they are not adding information and could be dropped from the pool of raters. This would be preferable to weighting their ratings because:
sometimes the |
50,744 | Combining ratings from multiple raters of different accuracy | If I am understanding correctly, you can analyze your data with a simple random intercept model. You have raters indexed by j from 1 to J and items indexed by i from 1 to I. For each item, each rater produces a response $ R_{ij} $ Using the terminology of psychometrics, it seems that you want to estimate the "difficult... | Combining ratings from multiple raters of different accuracy | If I am understanding correctly, you can analyze your data with a simple random intercept model. You have raters indexed by j from 1 to J and items indexed by i from 1 to I. For each item, each rater | Combining ratings from multiple raters of different accuracy
If I am understanding correctly, you can analyze your data with a simple random intercept model. You have raters indexed by j from 1 to J and items indexed by i from 1 to I. For each item, each rater produces a response $ R_{ij} $ Using the terminology of psy... | Combining ratings from multiple raters of different accuracy
If I am understanding correctly, you can analyze your data with a simple random intercept model. You have raters indexed by j from 1 to J and items indexed by i from 1 to I. For each item, each rater |
50,745 | Providing variance measures for speedup ratios | So, further research on this topic has led me to conclude that the correct way of doing this is going to involve Fieller's Theorem, which is for constructing the confidence interval of the ratio of two means --- a speedup ratio!
I've not completely worked this out, but for future people trying to figure this out, I'm... | Providing variance measures for speedup ratios | So, further research on this topic has led me to conclude that the correct way of doing this is going to involve Fieller's Theorem, which is for constructing the confidence interval of the ratio of t | Providing variance measures for speedup ratios
So, further research on this topic has led me to conclude that the correct way of doing this is going to involve Fieller's Theorem, which is for constructing the confidence interval of the ratio of two means --- a speedup ratio!
I've not completely worked this out, but f... | Providing variance measures for speedup ratios
So, further research on this topic has led me to conclude that the correct way of doing this is going to involve Fieller's Theorem, which is for constructing the confidence interval of the ratio of t |
50,746 | Providing variance measures for speedup ratios | Have you considered using candlesticks over the top of a trendline?
The candlestick body could be placed on each thread interval, and expanded in height a certain number of pixels per unit of the standard deviation. The relative size differences of the candlestick bodies would then demonstrate the change in deviation f... | Providing variance measures for speedup ratios | Have you considered using candlesticks over the top of a trendline?
The candlestick body could be placed on each thread interval, and expanded in height a certain number of pixels per unit of the stan | Providing variance measures for speedup ratios
Have you considered using candlesticks over the top of a trendline?
The candlestick body could be placed on each thread interval, and expanded in height a certain number of pixels per unit of the standard deviation. The relative size differences of the candlestick bodies w... | Providing variance measures for speedup ratios
Have you considered using candlesticks over the top of a trendline?
The candlestick body could be placed on each thread interval, and expanded in height a certain number of pixels per unit of the stan |
50,747 | Document classification with naive Bayes algorithm | You should construct your features (in this case, the words you're including as descriptors of each document) based only on your training set. This will calculate the probability of having a certain word given that it belongs to a particular class: $P(w_i|c_k)$. In case you're wondering, this probability is needed when... | Document classification with naive Bayes algorithm | You should construct your features (in this case, the words you're including as descriptors of each document) based only on your training set. This will calculate the probability of having a certain w | Document classification with naive Bayes algorithm
You should construct your features (in this case, the words you're including as descriptors of each document) based only on your training set. This will calculate the probability of having a certain word given that it belongs to a particular class: $P(w_i|c_k)$. In cas... | Document classification with naive Bayes algorithm
You should construct your features (in this case, the words you're including as descriptors of each document) based only on your training set. This will calculate the probability of having a certain w |
50,748 | Document classification with naive Bayes algorithm | You could first filter the stopwords and other meaningless frequent words, and then you could try some smaller amount and check how does it work. Generally, if you use big amount of words in your set, most of them will be pure noise and would not carry much information. Make few tries and check what rate is enough, but... | Document classification with naive Bayes algorithm | You could first filter the stopwords and other meaningless frequent words, and then you could try some smaller amount and check how does it work. Generally, if you use big amount of words in your set, | Document classification with naive Bayes algorithm
You could first filter the stopwords and other meaningless frequent words, and then you could try some smaller amount and check how does it work. Generally, if you use big amount of words in your set, most of them will be pure noise and would not carry much information... | Document classification with naive Bayes algorithm
You could first filter the stopwords and other meaningless frequent words, and then you could try some smaller amount and check how does it work. Generally, if you use big amount of words in your set, |
50,749 | Document classification with naive Bayes algorithm | order of variables is not an issue.I guess you are using the actual tokens as variables then randomforest or svm or any other model can understand that using variable names .THe issue can be when you dont have certain tokens in test data you might need to introduce dummy values | Document classification with naive Bayes algorithm | order of variables is not an issue.I guess you are using the actual tokens as variables then randomforest or svm or any other model can understand that using variable names .THe issue can be when you | Document classification with naive Bayes algorithm
order of variables is not an issue.I guess you are using the actual tokens as variables then randomforest or svm or any other model can understand that using variable names .THe issue can be when you dont have certain tokens in test data you might need to introduce dum... | Document classification with naive Bayes algorithm
order of variables is not an issue.I guess you are using the actual tokens as variables then randomforest or svm or any other model can understand that using variable names .THe issue can be when you |
50,750 | Joint distribution of sum of independent normals | Not entirely clear to me from reading the comments if the OP has solved this but there is no answer so I will write one.
The distribution of each $Y_i$ will be normal with given means and variances:
$\mu_0+\mu_1$ and $\sigma_0^2+\sigma^2_1$ for $Y_0$ and
$\mu_1+\mu_2$ and $\sigma_1^2+\sigma^2_2$ for $Y_1$. Now final... | Joint distribution of sum of independent normals | Not entirely clear to me from reading the comments if the OP has solved this but there is no answer so I will write one.
The distribution of each $Y_i$ will be normal with given means and variances: | Joint distribution of sum of independent normals
Not entirely clear to me from reading the comments if the OP has solved this but there is no answer so I will write one.
The distribution of each $Y_i$ will be normal with given means and variances:
$\mu_0+\mu_1$ and $\sigma_0^2+\sigma^2_1$ for $Y_0$ and
$\mu_1+\mu_2$... | Joint distribution of sum of independent normals
Not entirely clear to me from reading the comments if the OP has solved this but there is no answer so I will write one.
The distribution of each $Y_i$ will be normal with given means and variances: |
50,751 | Measuring 'synchrony' with time series correlations | Ok interesting question. Think I know a proper answer use Ramseyer and Tsachers model/method (Nonverbal Synchrony or Random Coincidence? How to Tell the Difference).
Your data seems excellent for it! Below a short description by head, might have some mistakes here so please read the referred papers as well.
They use M... | Measuring 'synchrony' with time series correlations | Ok interesting question. Think I know a proper answer use Ramseyer and Tsachers model/method (Nonverbal Synchrony or Random Coincidence? How to Tell the Difference).
Your data seems excellent for it! | Measuring 'synchrony' with time series correlations
Ok interesting question. Think I know a proper answer use Ramseyer and Tsachers model/method (Nonverbal Synchrony or Random Coincidence? How to Tell the Difference).
Your data seems excellent for it! Below a short description by head, might have some mistakes here so... | Measuring 'synchrony' with time series correlations
Ok interesting question. Think I know a proper answer use Ramseyer and Tsachers model/method (Nonverbal Synchrony or Random Coincidence? How to Tell the Difference).
Your data seems excellent for it! |
50,752 | Measuring 'synchrony' with time series correlations | Well, there are established measures for synchronization. There even is synchronization based clustering. Why don't you just use these measures?
Read up on ''Kuramoto model'':
http://en.wikipedia.org/wiki/Kuramoto_model | Measuring 'synchrony' with time series correlations | Well, there are established measures for synchronization. There even is synchronization based clustering. Why don't you just use these measures?
Read up on ''Kuramoto model'':
http://en.wikipedia.org/ | Measuring 'synchrony' with time series correlations
Well, there are established measures for synchronization. There even is synchronization based clustering. Why don't you just use these measures?
Read up on ''Kuramoto model'':
http://en.wikipedia.org/wiki/Kuramoto_model | Measuring 'synchrony' with time series correlations
Well, there are established measures for synchronization. There even is synchronization based clustering. Why don't you just use these measures?
Read up on ''Kuramoto model'':
http://en.wikipedia.org/ |
50,753 | Measuring 'synchrony' with time series correlations | Here's what I was suggesting in R code. I don't know what software you're working with, but at the very least you can download R for free and run the script pretty easily just to see what I was talking about and then create your own version. If you were in R, a lot of the loops could be replaced with the "rollapply" fu... | Measuring 'synchrony' with time series correlations | Here's what I was suggesting in R code. I don't know what software you're working with, but at the very least you can download R for free and run the script pretty easily just to see what I was talkin | Measuring 'synchrony' with time series correlations
Here's what I was suggesting in R code. I don't know what software you're working with, but at the very least you can download R for free and run the script pretty easily just to see what I was talking about and then create your own version. If you were in R, a lot of... | Measuring 'synchrony' with time series correlations
Here's what I was suggesting in R code. I don't know what software you're working with, but at the very least you can download R for free and run the script pretty easily just to see what I was talkin |
50,754 | Measuring 'synchrony' with time series correlations | A dynamic spontaneous synchronization type of visual would be useful here. Please see the example of firefly flashing simulation using star logo.
http://skyeome.net/wordpress/?p=56
http://education.mit.edu/starlogo/
You could use some measure of a member's pixel difference between frames (mean of difference in intens... | Measuring 'synchrony' with time series correlations | A dynamic spontaneous synchronization type of visual would be useful here. Please see the example of firefly flashing simulation using star logo.
http://skyeome.net/wordpress/?p=56
http://education. | Measuring 'synchrony' with time series correlations
A dynamic spontaneous synchronization type of visual would be useful here. Please see the example of firefly flashing simulation using star logo.
http://skyeome.net/wordpress/?p=56
http://education.mit.edu/starlogo/
You could use some measure of a member's pixel dif... | Measuring 'synchrony' with time series correlations
A dynamic spontaneous synchronization type of visual would be useful here. Please see the example of firefly flashing simulation using star logo.
http://skyeome.net/wordpress/?p=56
http://education. |
50,755 | Measuring 'synchrony' with time series correlations | I think it could be as simple as plotting the median activity level for the three participants. It wouldn't go up much just because one participant became active, but would go up much more if two or three participants were active. | Measuring 'synchrony' with time series correlations | I think it could be as simple as plotting the median activity level for the three participants. It wouldn't go up much just because one participant became active, but would go up much more if two or t | Measuring 'synchrony' with time series correlations
I think it could be as simple as plotting the median activity level for the three participants. It wouldn't go up much just because one participant became active, but would go up much more if two or three participants were active. | Measuring 'synchrony' with time series correlations
I think it could be as simple as plotting the median activity level for the three participants. It wouldn't go up much just because one participant became active, but would go up much more if two or t |
50,756 | Measuring 'synchrony' with time series correlations | You should use running correlations for pairs of individuals.
Here's an example:
Corbetta, D., & Thelen, E. (1996). The developmental origins of bimanual coordination: a dynamic perspective. Journal of Experimental Psychology: Human Perception and Performance, 22(2), 502-522.
You can do it easily on excel. Mail-me i... | Measuring 'synchrony' with time series correlations | You should use running correlations for pairs of individuals.
Here's an example:
Corbetta, D., & Thelen, E. (1996). The developmental origins of bimanual coordination: a dynamic perspective. Journal | Measuring 'synchrony' with time series correlations
You should use running correlations for pairs of individuals.
Here's an example:
Corbetta, D., & Thelen, E. (1996). The developmental origins of bimanual coordination: a dynamic perspective. Journal of Experimental Psychology: Human Perception and Performance, 22(2),... | Measuring 'synchrony' with time series correlations
You should use running correlations for pairs of individuals.
Here's an example:
Corbetta, D., & Thelen, E. (1996). The developmental origins of bimanual coordination: a dynamic perspective. Journal |
50,757 | Simple distance measure for financial time series | Consider calculating the squared difference of each daily return and taking the mean over all returns (mean square error). You could conisder each daily return to be an axis in a high dimensional space and user standard clustering techniques, e.g. k-means is the easiest to understand and implement and it may be suffici... | Simple distance measure for financial time series | Consider calculating the squared difference of each daily return and taking the mean over all returns (mean square error). You could conisder each daily return to be an axis in a high dimensional spac | Simple distance measure for financial time series
Consider calculating the squared difference of each daily return and taking the mean over all returns (mean square error). You could conisder each daily return to be an axis in a high dimensional space and user standard clustering techniques, e.g. k-means is the easiest... | Simple distance measure for financial time series
Consider calculating the squared difference of each daily return and taking the mean over all returns (mean square error). You could conisder each daily return to be an axis in a high dimensional spac |
50,758 | Simple distance measure for financial time series | For the record, this is an ongoing research topic. Here is a recent review on this question and some methods from the academic literature. | Simple distance measure for financial time series | For the record, this is an ongoing research topic. Here is a recent review on this question and some methods from the academic literature. | Simple distance measure for financial time series
For the record, this is an ongoing research topic. Here is a recent review on this question and some methods from the academic literature. | Simple distance measure for financial time series
For the record, this is an ongoing research topic. Here is a recent review on this question and some methods from the academic literature. |
50,759 | Simple distance measure for financial time series | You could take a look at cluster analysis. Essentially treat each strategy+system as an object and your goal is to cluster objects that are similar to one another in the same cluster.
A similarity metric that you could use is that of distance using the returns that a given strategy+system would give. Thus, distance bet... | Simple distance measure for financial time series | You could take a look at cluster analysis. Essentially treat each strategy+system as an object and your goal is to cluster objects that are similar to one another in the same cluster.
A similarity met | Simple distance measure for financial time series
You could take a look at cluster analysis. Essentially treat each strategy+system as an object and your goal is to cluster objects that are similar to one another in the same cluster.
A similarity metric that you could use is that of distance using the returns that a gi... | Simple distance measure for financial time series
You could take a look at cluster analysis. Essentially treat each strategy+system as an object and your goal is to cluster objects that are similar to one another in the same cluster.
A similarity met |
50,760 | Simple distance measure for financial time series | Please forgive me if I am not understanding the question, but I believe your "systems" are "strategies" that are being backtested or implemented. I cannot directly answer your question because I am not certain exactly what it is, so I will try and answer the one I think you are asking.
First, let me give you some obse... | Simple distance measure for financial time series | Please forgive me if I am not understanding the question, but I believe your "systems" are "strategies" that are being backtested or implemented. I cannot directly answer your question because I am n | Simple distance measure for financial time series
Please forgive me if I am not understanding the question, but I believe your "systems" are "strategies" that are being backtested or implemented. I cannot directly answer your question because I am not certain exactly what it is, so I will try and answer the one I thin... | Simple distance measure for financial time series
Please forgive me if I am not understanding the question, but I believe your "systems" are "strategies" that are being backtested or implemented. I cannot directly answer your question because I am n |
50,761 | Intervention analysis in time-series regression with seasonal ARIMA errors | The differencing implied by the denominator of your error term must be applied to $Y_t$, $S_t$ and $P_t$. That is, your model is equivalent to
$$
\nabla\nabla_{12}Y_t=\frac{\omega \nabla\nabla_{12}S_t}{1-\delta B}+\frac{\omega \nabla\nabla_{12}P_t}{1-\delta B}+\frac{\Theta(B)}{\Phi(B)} \eta_t,
$$
where $\nabla\nabla_... | Intervention analysis in time-series regression with seasonal ARIMA errors | The differencing implied by the denominator of your error term must be applied to $Y_t$, $S_t$ and $P_t$. That is, your model is equivalent to
$$
\nabla\nabla_{12}Y_t=\frac{\omega \nabla\nabla_{12}S_ | Intervention analysis in time-series regression with seasonal ARIMA errors
The differencing implied by the denominator of your error term must be applied to $Y_t$, $S_t$ and $P_t$. That is, your model is equivalent to
$$
\nabla\nabla_{12}Y_t=\frac{\omega \nabla\nabla_{12}S_t}{1-\delta B}+\frac{\omega \nabla\nabla_{12}... | Intervention analysis in time-series regression with seasonal ARIMA errors
The differencing implied by the denominator of your error term must be applied to $Y_t$, $S_t$ and $P_t$. That is, your model is equivalent to
$$
\nabla\nabla_{12}Y_t=\frac{\omega \nabla\nabla_{12}S_ |
50,762 | Intervention analysis in time-series regression with seasonal ARIMA errors | If you wish to estimate the model that you specified you would specify regular and seasonal differencing on Y and provide the two doubly integrated intervention series. It appears you are doing intervention modelling and not intervention detection prior to intervention modelling. The differencing operator in the noise ... | Intervention analysis in time-series regression with seasonal ARIMA errors | If you wish to estimate the model that you specified you would specify regular and seasonal differencing on Y and provide the two doubly integrated intervention series. It appears you are doing interv | Intervention analysis in time-series regression with seasonal ARIMA errors
If you wish to estimate the model that you specified you would specify regular and seasonal differencing on Y and provide the two doubly integrated intervention series. It appears you are doing intervention modelling and not intervention detecti... | Intervention analysis in time-series regression with seasonal ARIMA errors
If you wish to estimate the model that you specified you would specify regular and seasonal differencing on Y and provide the two doubly integrated intervention series. It appears you are doing interv |
50,763 | Intervention analysis in time-series regression with seasonal ARIMA errors | I don't know enough to totally parse your question, but I believe the usual practice is to first do a regression with indicator variables $S_t$ and $P_t$, then do your ARIMA on the residuals. | Intervention analysis in time-series regression with seasonal ARIMA errors | I don't know enough to totally parse your question, but I believe the usual practice is to first do a regression with indicator variables $S_t$ and $P_t$, then do your ARIMA on the residuals. | Intervention analysis in time-series regression with seasonal ARIMA errors
I don't know enough to totally parse your question, but I believe the usual practice is to first do a regression with indicator variables $S_t$ and $P_t$, then do your ARIMA on the residuals. | Intervention analysis in time-series regression with seasonal ARIMA errors
I don't know enough to totally parse your question, but I believe the usual practice is to first do a regression with indicator variables $S_t$ and $P_t$, then do your ARIMA on the residuals. |
50,764 | How to test unit root in a timeseries with unknown structural change? | Zivot Andrews tests the alternative of a one time structural break against a null of a unit root process. Variations in the ZA paper test for a change in the intercept, in the trend, or in the intercept and the trend.
ZA endogenously selects the break point based on the point in time that gives the most weight to the a... | How to test unit root in a timeseries with unknown structural change? | Zivot Andrews tests the alternative of a one time structural break against a null of a unit root process. Variations in the ZA paper test for a change in the intercept, in the trend, or in the interce | How to test unit root in a timeseries with unknown structural change?
Zivot Andrews tests the alternative of a one time structural break against a null of a unit root process. Variations in the ZA paper test for a change in the intercept, in the trend, or in the intercept and the trend.
ZA endogenously selects the brea... | How to test unit root in a timeseries with unknown structural change?
Zivot Andrews tests the alternative of a one time structural break against a null of a unit root process. Variations in the ZA paper test for a change in the intercept, in the trend, or in the interce |
50,765 | Weighted regression for categorical variables | You should not define weights by hand. Use the gls function from nlme (see help, you probably want option weights = varIdent(form = ~ 1 | group) ) to estimate the weights, and then use Pearson residuals (which divide the raw residual by the expected / fitted variance) to check the model. | Weighted regression for categorical variables | You should not define weights by hand. Use the gls function from nlme (see help, you probably want option weights = varIdent(form = ~ 1 | group) ) to estimate the weights, and then use Pearson residua | Weighted regression for categorical variables
You should not define weights by hand. Use the gls function from nlme (see help, you probably want option weights = varIdent(form = ~ 1 | group) ) to estimate the weights, and then use Pearson residuals (which divide the raw residual by the expected / fitted variance) to ch... | Weighted regression for categorical variables
You should not define weights by hand. Use the gls function from nlme (see help, you probably want option weights = varIdent(form = ~ 1 | group) ) to estimate the weights, and then use Pearson residua |
50,766 | Using the sde package in R to simulate a SV model with leverage | Hull-White/Vasicek Model: dX(t) = 3*(2-x)*dt+ 2*dw(t)
> library(Sim.DiffProc)
> drift <- expression( (3*(2-x)) )
> diffusion <- expression( (2) )
> snssde(N=1000,M=1,T=1,t0=0,x0=10,Dt=0.001,drift,diffusion,Output=FALSE)
Multiple trajectories of the OU process by Euler Scheme
> snssde(N=1000,M=50,T=1,t0=0,x0=10,Dt=0.00... | Using the sde package in R to simulate a SV model with leverage | Hull-White/Vasicek Model: dX(t) = 3*(2-x)*dt+ 2*dw(t)
> library(Sim.DiffProc)
> drift <- expression( (3*(2-x)) )
> diffusion <- expression( (2) )
> snssde(N=1000,M=1,T=1,t0=0,x0=10,Dt=0.001,drift,diff | Using the sde package in R to simulate a SV model with leverage
Hull-White/Vasicek Model: dX(t) = 3*(2-x)*dt+ 2*dw(t)
> library(Sim.DiffProc)
> drift <- expression( (3*(2-x)) )
> diffusion <- expression( (2) )
> snssde(N=1000,M=1,T=1,t0=0,x0=10,Dt=0.001,drift,diffusion,Output=FALSE)
Multiple trajectories of the OU pro... | Using the sde package in R to simulate a SV model with leverage
Hull-White/Vasicek Model: dX(t) = 3*(2-x)*dt+ 2*dw(t)
> library(Sim.DiffProc)
> drift <- expression( (3*(2-x)) )
> diffusion <- expression( (2) )
> snssde(N=1000,M=1,T=1,t0=0,x0=10,Dt=0.001,drift,diff |
50,767 | Weighted spatial clustering | For anybody who wants to know the answer, this is what I finally did:
I implemented a normal K-Means algorithm, but with some modifications:
The calculation of the centroid is site = Sum(p * weight^alpha) / Sum(weight^alpha) for all the points that belong to that site.
The calculation of the squared distance between p... | Weighted spatial clustering | For anybody who wants to know the answer, this is what I finally did:
I implemented a normal K-Means algorithm, but with some modifications:
The calculation of the centroid is site = Sum(p * weight^a | Weighted spatial clustering
For anybody who wants to know the answer, this is what I finally did:
I implemented a normal K-Means algorithm, but with some modifications:
The calculation of the centroid is site = Sum(p * weight^alpha) / Sum(weight^alpha) for all the points that belong to that site.
The calculation of th... | Weighted spatial clustering
For anybody who wants to know the answer, this is what I finally did:
I implemented a normal K-Means algorithm, but with some modifications:
The calculation of the centroid is site = Sum(p * weight^a |
50,768 | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks in each year | Packages zoo and xts handle arbitrary time indices. Pick a day of the week that will reflect the discrepancy (late enough to already be in the first week of 2009 yet early enough to be in the last week of 2009) and add it to your date. Functions zoo() or xts() will then accept the date argument after as.Date() is appli... | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks | Packages zoo and xts handle arbitrary time indices. Pick a day of the week that will reflect the discrepancy (late enough to already be in the first week of 2009 yet early enough to be in the last wee | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks in each year
Packages zoo and xts handle arbitrary time indices. Pick a day of the week that will reflect the discrepancy (late enough to already be in the first week of 2009 yet early enough to be in the last week of 20... | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks
Packages zoo and xts handle arbitrary time indices. Pick a day of the week that will reflect the discrepancy (late enough to already be in the first week of 2009 yet early enough to be in the last wee |
50,769 | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks in each year | I struggled with this for a while with a problem I was working on, and in the end decided that it was better to aggregate into monthly data. It (mostly) solves the number-of-weeks problem and it helped smooth out the noise so the results were better anyhow.
An added benefit is that people have a lot of context for mont... | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks | I struggled with this for a while with a problem I was working on, and in the end decided that it was better to aggregate into monthly data. It (mostly) solves the number-of-weeks problem and it helpe | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks in each year
I struggled with this for a while with a problem I was working on, and in the end decided that it was better to aggregate into monthly data. It (mostly) solves the number-of-weeks problem and it helped smoot... | Time series modeling in R on a weekly basis over multiple years featuring different number of weeks
I struggled with this for a while with a problem I was working on, and in the end decided that it was better to aggregate into monthly data. It (mostly) solves the number-of-weeks problem and it helpe |
50,770 | Problem in evaluating naive Bayes | Well: naive Bayes is called naive for a reason: the assumed conditional independence is often doubtful, even though it turns out to work well in a lot of practical cases.
Besides that: you have "chosen" your conditional probabilities so that it turns out this way. There is no (a priori) reason why P(tennis|News) and P(... | Problem in evaluating naive Bayes | Well: naive Bayes is called naive for a reason: the assumed conditional independence is often doubtful, even though it turns out to work well in a lot of practical cases.
Besides that: you have "chose | Problem in evaluating naive Bayes
Well: naive Bayes is called naive for a reason: the assumed conditional independence is often doubtful, even though it turns out to work well in a lot of practical cases.
Besides that: you have "chosen" your conditional probabilities so that it turns out this way. There is no (a priori... | Problem in evaluating naive Bayes
Well: naive Bayes is called naive for a reason: the assumed conditional independence is often doubtful, even though it turns out to work well in a lot of practical cases.
Besides that: you have "chose |
50,771 | Problem in evaluating naive Bayes | A naive Bayes classifier, as the names suggests, is a simple application of Bayes' Theorem. Basically, it calculates the probabilities of quantities of interest (generally unobserved, called parameters or latent classes) based on the observed data. In your case the observed data are: news, football, and tennis. The qua... | Problem in evaluating naive Bayes | A naive Bayes classifier, as the names suggests, is a simple application of Bayes' Theorem. Basically, it calculates the probabilities of quantities of interest (generally unobserved, called parameter | Problem in evaluating naive Bayes
A naive Bayes classifier, as the names suggests, is a simple application of Bayes' Theorem. Basically, it calculates the probabilities of quantities of interest (generally unobserved, called parameters or latent classes) based on the observed data. In your case the observed data are: n... | Problem in evaluating naive Bayes
A naive Bayes classifier, as the names suggests, is a simple application of Bayes' Theorem. Basically, it calculates the probabilities of quantities of interest (generally unobserved, called parameter |
50,772 | Is there a generic term for measures of correctness like "precision" and "recall"? | I don't know if there is a generally accepted generic term, but I think you might say "classifier performance metrics/measures" (like in the R package ROCR), or "measures of predictive/classification performance".
The widely cited paper by Fawcett, for example, talks about "common performance metrics" and lists true po... | Is there a generic term for measures of correctness like "precision" and "recall"? | I don't know if there is a generally accepted generic term, but I think you might say "classifier performance metrics/measures" (like in the R package ROCR), or "measures of predictive/classification | Is there a generic term for measures of correctness like "precision" and "recall"?
I don't know if there is a generally accepted generic term, but I think you might say "classifier performance metrics/measures" (like in the R package ROCR), or "measures of predictive/classification performance".
The widely cited paper ... | Is there a generic term for measures of correctness like "precision" and "recall"?
I don't know if there is a generally accepted generic term, but I think you might say "classifier performance metrics/measures" (like in the R package ROCR), or "measures of predictive/classification |
50,773 | Is there a generic term for measures of correctness like "precision" and "recall"? | I would use precision and recall but explain it with a dart board analogy if necessary. | Is there a generic term for measures of correctness like "precision" and "recall"? | I would use precision and recall but explain it with a dart board analogy if necessary. | Is there a generic term for measures of correctness like "precision" and "recall"?
I would use precision and recall but explain it with a dart board analogy if necessary. | Is there a generic term for measures of correctness like "precision" and "recall"?
I would use precision and recall but explain it with a dart board analogy if necessary. |
50,774 | Estimating correlated parameters with multi-level model | Have you tried to use Bugs or Jags, calling one of them from R? The model you seem to be estimating is a simple varying slope model, with predictors at the second level.
I'd rewrite your model as:
Be $i = 1, ...n$ students and $k = 1, ... K$ classes. Assuming your data is in the form student-class (i.e. repeated measur... | Estimating correlated parameters with multi-level model | Have you tried to use Bugs or Jags, calling one of them from R? The model you seem to be estimating is a simple varying slope model, with predictors at the second level.
I'd rewrite your model as:
Be | Estimating correlated parameters with multi-level model
Have you tried to use Bugs or Jags, calling one of them from R? The model you seem to be estimating is a simple varying slope model, with predictors at the second level.
I'd rewrite your model as:
Be $i = 1, ...n$ students and $k = 1, ... K$ classes. Assuming your... | Estimating correlated parameters with multi-level model
Have you tried to use Bugs or Jags, calling one of them from R? The model you seem to be estimating is a simple varying slope model, with predictors at the second level.
I'd rewrite your model as:
Be |
50,775 | Estimating correlated parameters with multi-level model | How about just writing out the likelihood function and maximizing? | Estimating correlated parameters with multi-level model | How about just writing out the likelihood function and maximizing? | Estimating correlated parameters with multi-level model
How about just writing out the likelihood function and maximizing? | Estimating correlated parameters with multi-level model
How about just writing out the likelihood function and maximizing? |
50,776 | Estimating correlated parameters with multi-level model | How is this advantageous over a normal varying coefficient model such as:
fit<-lmer(score~1+vector of class_attributes+vector of student attributes
+(1+vector of class attributes+vector of student attributes)
+(1+vector of student attributes|class)
+(1+vector of class attributes|student))
?
In this example, there is a... | Estimating correlated parameters with multi-level model | How is this advantageous over a normal varying coefficient model such as:
fit<-lmer(score~1+vector of class_attributes+vector of student attributes
+(1+vector of class attributes+vector of student att | Estimating correlated parameters with multi-level model
How is this advantageous over a normal varying coefficient model such as:
fit<-lmer(score~1+vector of class_attributes+vector of student attributes
+(1+vector of class attributes+vector of student attributes)
+(1+vector of student attributes|class)
+(1+vector of c... | Estimating correlated parameters with multi-level model
How is this advantageous over a normal varying coefficient model such as:
fit<-lmer(score~1+vector of class_attributes+vector of student attributes
+(1+vector of class attributes+vector of student att |
50,777 | Which statistical test should I use for my experiment on aggressive interactions in killifish? | Sophie and I discussed this earlier (she is a student at my university) and I am still not satisfied with any of my suggestions so far. Here are two possibilities for the winner/loser data (assuming you always have a winner).
1) Compete each yellow against each red (64 competitions) and record which colour won. Test ... | Which statistical test should I use for my experiment on aggressive interactions in killifish? | Sophie and I discussed this earlier (she is a student at my university) and I am still not satisfied with any of my suggestions so far. Here are two possibilities for the winner/loser data (assuming | Which statistical test should I use for my experiment on aggressive interactions in killifish?
Sophie and I discussed this earlier (she is a student at my university) and I am still not satisfied with any of my suggestions so far. Here are two possibilities for the winner/loser data (assuming you always have a winner)... | Which statistical test should I use for my experiment on aggressive interactions in killifish?
Sophie and I discussed this earlier (she is a student at my university) and I am still not satisfied with any of my suggestions so far. Here are two possibilities for the winner/loser data (assuming |
50,778 | Which statistical test should I use for my experiment on aggressive interactions in killifish? | You might consider doing a round robin tournament and then estimating the effect of color controlling for weight within a hierarchical paired comparison model. With 120 comparisons, you still will not have much power, but you'll have more than the non-parametric techniques. You can get a little bit more power by having... | Which statistical test should I use for my experiment on aggressive interactions in killifish? | You might consider doing a round robin tournament and then estimating the effect of color controlling for weight within a hierarchical paired comparison model. With 120 comparisons, you still will not | Which statistical test should I use for my experiment on aggressive interactions in killifish?
You might consider doing a round robin tournament and then estimating the effect of color controlling for weight within a hierarchical paired comparison model. With 120 comparisons, you still will not have much power, but you... | Which statistical test should I use for my experiment on aggressive interactions in killifish?
You might consider doing a round robin tournament and then estimating the effect of color controlling for weight within a hierarchical paired comparison model. With 120 comparisons, you still will not |
50,779 | GLM for proportional data | Logistic regression, like this is, assumes a binomial distribution, or, as I prefer, a Bernoulli distribution per event. I know of no case nor reason where this should not be safely assumed by itself (either it happens or it doesn't, and in a population you can always assign a probability to this). There is no reason t... | GLM for proportional data | Logistic regression, like this is, assumes a binomial distribution, or, as I prefer, a Bernoulli distribution per event. I know of no case nor reason where this should not be safely assumed by itself | GLM for proportional data
Logistic regression, like this is, assumes a binomial distribution, or, as I prefer, a Bernoulli distribution per event. I know of no case nor reason where this should not be safely assumed by itself (either it happens or it doesn't, and in a population you can always assign a probability to t... | GLM for proportional data
Logistic regression, like this is, assumes a binomial distribution, or, as I prefer, a Bernoulli distribution per event. I know of no case nor reason where this should not be safely assumed by itself |
50,780 | Probability distribution of fragment lengths | Let the rod have length $L$ and fix a segment of length $x$. The chance that any single breakpoint misses the segment equals the proportion of the rod not occupied by the segment, $1−x/L$. Because the breakpoints are independent, the chance that all of them miss it is the product of $n$ such chances, $(1 - x/L)^n$.
... | Probability distribution of fragment lengths | Let the rod have length $L$ and fix a segment of length $x$. The chance that any single breakpoint misses the segment equals the proportion of the rod not occupied by the segment, $1−x/L$. Because t | Probability distribution of fragment lengths
Let the rod have length $L$ and fix a segment of length $x$. The chance that any single breakpoint misses the segment equals the proportion of the rod not occupied by the segment, $1−x/L$. Because the breakpoints are independent, the chance that all of them miss it is the ... | Probability distribution of fragment lengths
Let the rod have length $L$ and fix a segment of length $x$. The chance that any single breakpoint misses the segment equals the proportion of the rod not occupied by the segment, $1−x/L$. Because t |
50,781 | Probability distribution of fragment lengths | Let $\{X_i\}$ be the locations of the cuts.
I'd approach this problem by finding the order statistics $\{Y_i\}$ so that $Y_1$ would be the location of the leftmost cut. Then I'd calculate the probability distributions of the differences between the variables $Y_i-Y_{i-1}$. Don't forget to also calculate $Y_1-0$ and $L-... | Probability distribution of fragment lengths | Let $\{X_i\}$ be the locations of the cuts.
I'd approach this problem by finding the order statistics $\{Y_i\}$ so that $Y_1$ would be the location of the leftmost cut. Then I'd calculate the probabil | Probability distribution of fragment lengths
Let $\{X_i\}$ be the locations of the cuts.
I'd approach this problem by finding the order statistics $\{Y_i\}$ so that $Y_1$ would be the location of the leftmost cut. Then I'd calculate the probability distributions of the differences between the variables $Y_i-Y_{i-1}$. D... | Probability distribution of fragment lengths
Let $\{X_i\}$ be the locations of the cuts.
I'd approach this problem by finding the order statistics $\{Y_i\}$ so that $Y_1$ would be the location of the leftmost cut. Then I'd calculate the probabil |
50,782 | Statistical test for a series of data over time | As GaBorgulya pointed out one needs to have a model to detect the potential anomaly. This model needs to generate a "white noise" error series or be sufficient to separate signal and noise. With this model in hand based upon older data one could then compare the new value with the prediction interval. This is the class... | Statistical test for a series of data over time | As GaBorgulya pointed out one needs to have a model to detect the potential anomaly. This model needs to generate a "white noise" error series or be sufficient to separate signal and noise. With this | Statistical test for a series of data over time
As GaBorgulya pointed out one needs to have a model to detect the potential anomaly. This model needs to generate a "white noise" error series or be sufficient to separate signal and noise. With this model in hand based upon older data one could then compare the new value... | Statistical test for a series of data over time
As GaBorgulya pointed out one needs to have a model to detect the potential anomaly. This model needs to generate a "white noise" error series or be sufficient to separate signal and noise. With this |
50,783 | Statistical test for a series of data over time | With less than a year of data, it'll be impossible to account for any kind of yearly seasonal effect. (For example, if your data was shopping-related, you would have things like annual holidays, perhaps two sales a year, etc.)
You might want to look at Statistical Process Control tools like http://en.wikipedia.org/wiki... | Statistical test for a series of data over time | With less than a year of data, it'll be impossible to account for any kind of yearly seasonal effect. (For example, if your data was shopping-related, you would have things like annual holidays, perha | Statistical test for a series of data over time
With less than a year of data, it'll be impossible to account for any kind of yearly seasonal effect. (For example, if your data was shopping-related, you would have things like annual holidays, perhaps two sales a year, etc.)
You might want to look at Statistical Process... | Statistical test for a series of data over time
With less than a year of data, it'll be impossible to account for any kind of yearly seasonal effect. (For example, if your data was shopping-related, you would have things like annual holidays, perha |
50,784 | When is a randomised controlled trial (RCT) balanced? | I have always seen "balance" for a clinical trial described as you suggested - that there is some difference in the covariate patterns between the treatment and control arm. Note however, that there are ways this can arise beyond just misfortune during randomization. Two that come to mind quickly are:
Time-varying con... | When is a randomised controlled trial (RCT) balanced? | I have always seen "balance" for a clinical trial described as you suggested - that there is some difference in the covariate patterns between the treatment and control arm. Note however, that there a | When is a randomised controlled trial (RCT) balanced?
I have always seen "balance" for a clinical trial described as you suggested - that there is some difference in the covariate patterns between the treatment and control arm. Note however, that there are ways this can arise beyond just misfortune during randomization... | When is a randomised controlled trial (RCT) balanced?
I have always seen "balance" for a clinical trial described as you suggested - that there is some difference in the covariate patterns between the treatment and control arm. Note however, that there a |
50,785 | When is a randomised controlled trial (RCT) balanced? | Balanced designs have really just one goal, orthogonal treatment effects. Orthogonal design lowers the risk of unobservables sneaking into your effect estimates in an uneven way. See: http://www1.umn.edu/statsoft/doc/statnotes/stat06.txt for an excellent discussion of this topic. | When is a randomised controlled trial (RCT) balanced? | Balanced designs have really just one goal, orthogonal treatment effects. Orthogonal design lowers the risk of unobservables sneaking into your effect estimates in an uneven way. See: http://www1.umn. | When is a randomised controlled trial (RCT) balanced?
Balanced designs have really just one goal, orthogonal treatment effects. Orthogonal design lowers the risk of unobservables sneaking into your effect estimates in an uneven way. See: http://www1.umn.edu/statsoft/doc/statnotes/stat06.txt for an excellent discussion ... | When is a randomised controlled trial (RCT) balanced?
Balanced designs have really just one goal, orthogonal treatment effects. Orthogonal design lowers the risk of unobservables sneaking into your effect estimates in an uneven way. See: http://www1.umn. |
50,786 | Estimating event probability from historical time series with clear seasonality | I think the joint distribution of temperature data on successive days could be reasonably modelled using a multi-variate Gaussian (Gaussian distributions are often used in statistical downscaling of temperature). What I would try would be to regress the mean and covariance matrix of the temperature time series on sine... | Estimating event probability from historical time series with clear seasonality | I think the joint distribution of temperature data on successive days could be reasonably modelled using a multi-variate Gaussian (Gaussian distributions are often used in statistical downscaling of t | Estimating event probability from historical time series with clear seasonality
I think the joint distribution of temperature data on successive days could be reasonably modelled using a multi-variate Gaussian (Gaussian distributions are often used in statistical downscaling of temperature). What I would try would be ... | Estimating event probability from historical time series with clear seasonality
I think the joint distribution of temperature data on successive days could be reasonably modelled using a multi-variate Gaussian (Gaussian distributions are often used in statistical downscaling of t |
50,787 | Estimating event probability from historical time series with clear seasonality | I know little about meteorology, so my following assumptions may be wrong: today's temperature is similar to yesterday's and the day before yesterday's (maybe more days going back), and also similar to temperature a year age, two years ago, three years ago, etc.
If these assumptions got reinforcement I would use an ARM... | Estimating event probability from historical time series with clear seasonality | I know little about meteorology, so my following assumptions may be wrong: today's temperature is similar to yesterday's and the day before yesterday's (maybe more days going back), and also similar t | Estimating event probability from historical time series with clear seasonality
I know little about meteorology, so my following assumptions may be wrong: today's temperature is similar to yesterday's and the day before yesterday's (maybe more days going back), and also similar to temperature a year age, two years ago,... | Estimating event probability from historical time series with clear seasonality
I know little about meteorology, so my following assumptions may be wrong: today's temperature is similar to yesterday's and the day before yesterday's (maybe more days going back), and also similar t |
50,788 | Is there a classification of physical measurements according to their statistical distribution? | Some people have started to look at this issue in the chemometrics literature. For instance, about 20 years ago Robert Gibbons started to do statistical analyses suggesting instrument responses (for low-level measurement of chemicals) were nonlinear, heteroscedastic, and had non-normal (perhaps lognormal) error distri... | Is there a classification of physical measurements according to their statistical distribution? | Some people have started to look at this issue in the chemometrics literature. For instance, about 20 years ago Robert Gibbons started to do statistical analyses suggesting instrument responses (for | Is there a classification of physical measurements according to their statistical distribution?
Some people have started to look at this issue in the chemometrics literature. For instance, about 20 years ago Robert Gibbons started to do statistical analyses suggesting instrument responses (for low-level measurement of... | Is there a classification of physical measurements according to their statistical distribution?
Some people have started to look at this issue in the chemometrics literature. For instance, about 20 years ago Robert Gibbons started to do statistical analyses suggesting instrument responses (for |
50,789 | Correlation between two nodes of a single layer MLP for joint-Gaussian input | The question really concerns pairs of normal variates. Let's call them $x_1$ and $x_2$ with means $\mu_i$, standard deviations $\sigma_i$, and correlation $\rho$. Whence their joint pdf is
$$\frac{1}{2 \pi \sqrt{1 - \rho^2} \sigma_1 \sigma_2}
e^{-\frac{1}{1-\rho^2} \left(\frac{(x_1 - \mu_1)^2}{2 \sigma_1^2} + \frac{... | Correlation between two nodes of a single layer MLP for joint-Gaussian input | The question really concerns pairs of normal variates. Let's call them $x_1$ and $x_2$ with means $\mu_i$, standard deviations $\sigma_i$, and correlation $\rho$. Whence their joint pdf is
$$\frac{1 | Correlation between two nodes of a single layer MLP for joint-Gaussian input
The question really concerns pairs of normal variates. Let's call them $x_1$ and $x_2$ with means $\mu_i$, standard deviations $\sigma_i$, and correlation $\rho$. Whence their joint pdf is
$$\frac{1}{2 \pi \sqrt{1 - \rho^2} \sigma_1 \sigma_2... | Correlation between two nodes of a single layer MLP for joint-Gaussian input
The question really concerns pairs of normal variates. Let's call them $x_1$ and $x_2$ with means $\mu_i$, standard deviations $\sigma_i$, and correlation $\rho$. Whence their joint pdf is
$$\frac{1 |
50,790 | Measuring and analyzing sample complexity | Let's say we want to bound empirical risk of a model. Given an arbitrary $(\epsilon, \delta)$, the sample complexity is $n(\epsilon, \delta)$ such that for $n\geq n(\epsilon, \delta)$
$$
P(|\hat{L}(f) - L(f) | \geq \epsilon ) \leq \delta
$$
The function $\delta(n,\epsilon)$ is a bound on the deviation from the main (u... | Measuring and analyzing sample complexity | Let's say we want to bound empirical risk of a model. Given an arbitrary $(\epsilon, \delta)$, the sample complexity is $n(\epsilon, \delta)$ such that for $n\geq n(\epsilon, \delta)$
$$
P(|\hat{L}(f | Measuring and analyzing sample complexity
Let's say we want to bound empirical risk of a model. Given an arbitrary $(\epsilon, \delta)$, the sample complexity is $n(\epsilon, \delta)$ such that for $n\geq n(\epsilon, \delta)$
$$
P(|\hat{L}(f) - L(f) | \geq \epsilon ) \leq \delta
$$
The function $\delta(n,\epsilon)$ is... | Measuring and analyzing sample complexity
Let's say we want to bound empirical risk of a model. Given an arbitrary $(\epsilon, \delta)$, the sample complexity is $n(\epsilon, \delta)$ such that for $n\geq n(\epsilon, \delta)$
$$
P(|\hat{L}(f |
50,791 | Kolmogorov-Smirnov and lattice paths | To add to @Cardinal 's answers in the comments, I think there is work that addresses the "claim the null distribution of the Kolmogorov-Smirnov maps onto another lattice path problem that could be solved by a "two-sided ballot theorem" and "is there a general framework around all of this? The two-sample KS test?":
This... | Kolmogorov-Smirnov and lattice paths | To add to @Cardinal 's answers in the comments, I think there is work that addresses the "claim the null distribution of the Kolmogorov-Smirnov maps onto another lattice path problem that could be sol | Kolmogorov-Smirnov and lattice paths
To add to @Cardinal 's answers in the comments, I think there is work that addresses the "claim the null distribution of the Kolmogorov-Smirnov maps onto another lattice path problem that could be solved by a "two-sided ballot theorem" and "is there a general framework around all of... | Kolmogorov-Smirnov and lattice paths
To add to @Cardinal 's answers in the comments, I think there is work that addresses the "claim the null distribution of the Kolmogorov-Smirnov maps onto another lattice path problem that could be sol |
50,792 | Geostatistical analysis using spatial.exp in WinBugs | I have worked this out myself.
The lower bound for phi can be estiamted from
-ln(0.5)/(max separating distance between points)
To find the max separating distance I used the following code in R. My data are in a flat file with x and y coords renamed to long and lat respectively:
data <- read.csv(file="file.csv", hea... | Geostatistical analysis using spatial.exp in WinBugs | I have worked this out myself.
The lower bound for phi can be estiamted from
-ln(0.5)/(max separating distance between points)
To find the max separating distance I used the following code in R. My | Geostatistical analysis using spatial.exp in WinBugs
I have worked this out myself.
The lower bound for phi can be estiamted from
-ln(0.5)/(max separating distance between points)
To find the max separating distance I used the following code in R. My data are in a flat file with x and y coords renamed to long and la... | Geostatistical analysis using spatial.exp in WinBugs
I have worked this out myself.
The lower bound for phi can be estiamted from
-ln(0.5)/(max separating distance between points)
To find the max separating distance I used the following code in R. My |
50,793 | How to do a repeated measures multinomial logistic regression using SPSS? | One way is to build an SPSS PLUM or NOMREG model that checks for an interaction between each predictor and a binary predictor, “time.” In that scenario you'd use just a single column for all the values of your outcome variable. For 1/2 the data set, time would be marked 0, and for the other half it'd be marked 1. Es... | How to do a repeated measures multinomial logistic regression using SPSS? | One way is to build an SPSS PLUM or NOMREG model that checks for an interaction between each predictor and a binary predictor, “time.” In that scenario you'd use just a single column for all the valu | How to do a repeated measures multinomial logistic regression using SPSS?
One way is to build an SPSS PLUM or NOMREG model that checks for an interaction between each predictor and a binary predictor, “time.” In that scenario you'd use just a single column for all the values of your outcome variable. For 1/2 the data... | How to do a repeated measures multinomial logistic regression using SPSS?
One way is to build an SPSS PLUM or NOMREG model that checks for an interaction between each predictor and a binary predictor, “time.” In that scenario you'd use just a single column for all the valu |
50,794 | Non-parametric regression | If your response variable is ordinal, you may want to consider and "ordered logistic regression". This is basically where you model the cumulative probabilities {in the simple example, you would model $Pr(Y\leq 1),Pr(Y\leq 2),Pr(Y\leq 3)$}. This incorporates the ordering of the response into the model, without the ne... | Non-parametric regression | If your response variable is ordinal, you may want to consider and "ordered logistic regression". This is basically where you model the cumulative probabilities {in the simple example, you would mode | Non-parametric regression
If your response variable is ordinal, you may want to consider and "ordered logistic regression". This is basically where you model the cumulative probabilities {in the simple example, you would model $Pr(Y\leq 1),Pr(Y\leq 2),Pr(Y\leq 3)$}. This incorporates the ordering of the response into... | Non-parametric regression
If your response variable is ordinal, you may want to consider and "ordered logistic regression". This is basically where you model the cumulative probabilities {in the simple example, you would mode |
50,795 | Experiment design for proportion | Thank you, whuber, for making me aware of Wald's Sequential Probability Ratio Test (SPRT). At your recommendation, I will relist this Quantitative Skills site. They will give you an out-of-the-box table to determine whether to continue or stop testing.
I also took the time to research that site's references, and was di... | Experiment design for proportion | Thank you, whuber, for making me aware of Wald's Sequential Probability Ratio Test (SPRT). At your recommendation, I will relist this Quantitative Skills site. They will give you an out-of-the-box tab | Experiment design for proportion
Thank you, whuber, for making me aware of Wald's Sequential Probability Ratio Test (SPRT). At your recommendation, I will relist this Quantitative Skills site. They will give you an out-of-the-box table to determine whether to continue or stop testing.
I also took the time to research t... | Experiment design for proportion
Thank you, whuber, for making me aware of Wald's Sequential Probability Ratio Test (SPRT). At your recommendation, I will relist this Quantitative Skills site. They will give you an out-of-the-box tab |
50,796 | Nonparametric sign test for correlated variables | Under one interpretation of your situation there is no need to modify the p values at all.
For example, let's posit that a sequence of (unknown) bivariate distributions $p_i(x,y)$ govern $A$ and $B$ for each organism $i$. That is, $\Pr(A=x, B=y) = p_i(x,y)$ for all possible outcomes $(x,y)$ of $(A,B)$. To test whethe... | Nonparametric sign test for correlated variables | Under one interpretation of your situation there is no need to modify the p values at all.
For example, let's posit that a sequence of (unknown) bivariate distributions $p_i(x,y)$ govern $A$ and $B$ f | Nonparametric sign test for correlated variables
Under one interpretation of your situation there is no need to modify the p values at all.
For example, let's posit that a sequence of (unknown) bivariate distributions $p_i(x,y)$ govern $A$ and $B$ for each organism $i$. That is, $\Pr(A=x, B=y) = p_i(x,y)$ for all poss... | Nonparametric sign test for correlated variables
Under one interpretation of your situation there is no need to modify the p values at all.
For example, let's posit that a sequence of (unknown) bivariate distributions $p_i(x,y)$ govern $A$ and $B$ f |
50,797 | Is there any relation between Power Law and Negative Binomial distribution? | There are many power-law distributions, so you have a lot of possible models. You might start by trying to fit a log-series distribution, which is a limiting case of the negative binomial.
Don't think a priori that you have a mixture distribution as suggested by whuber until you've estimated model parameters and don... | Is there any relation between Power Law and Negative Binomial distribution? | There are many power-law distributions, so you have a lot of possible models. You might start by trying to fit a log-series distribution, which is a limiting case of the negative binomial.
Don't th | Is there any relation between Power Law and Negative Binomial distribution?
There are many power-law distributions, so you have a lot of possible models. You might start by trying to fit a log-series distribution, which is a limiting case of the negative binomial.
Don't think a priori that you have a mixture distrib... | Is there any relation between Power Law and Negative Binomial distribution?
There are many power-law distributions, so you have a lot of possible models. You might start by trying to fit a log-series distribution, which is a limiting case of the negative binomial.
Don't th |
50,798 | Stochastic coordinate descent for $\ell_1$ regularization | I believe that in the specific case of L2 loss (ordinary linear regression), the convergence rate of coordinate descent will depend on the correlation structure of the predictors ($X_i$’s). Consider the case where they are uncorrelated. Then cyclic coordinate descent converges after one cycle.
Another heuristic that ... | Stochastic coordinate descent for $\ell_1$ regularization | I believe that in the specific case of L2 loss (ordinary linear regression), the convergence rate of coordinate descent will depend on the correlation structure of the predictors ($X_i$’s). Consider | Stochastic coordinate descent for $\ell_1$ regularization
I believe that in the specific case of L2 loss (ordinary linear regression), the convergence rate of coordinate descent will depend on the correlation structure of the predictors ($X_i$’s). Consider the case where they are uncorrelated. Then cyclic coordinate ... | Stochastic coordinate descent for $\ell_1$ regularization
I believe that in the specific case of L2 loss (ordinary linear regression), the convergence rate of coordinate descent will depend on the correlation structure of the predictors ($X_i$’s). Consider |
50,799 | How to check that a sample suits multi-dimensional uniform distribution? | For the 1D continuous uniform distribution U(a,b) the uniformly minimum variance unbiased (UMVU) estimates of a and b can be obtained in closed form by a straightforward example of maximum spacing estimation. Can't see any reason that applying this separately for each dimension wouldn't give you UMVU estimates of all p... | How to check that a sample suits multi-dimensional uniform distribution? | For the 1D continuous uniform distribution U(a,b) the uniformly minimum variance unbiased (UMVU) estimates of a and b can be obtained in closed form by a straightforward example of maximum spacing est | How to check that a sample suits multi-dimensional uniform distribution?
For the 1D continuous uniform distribution U(a,b) the uniformly minimum variance unbiased (UMVU) estimates of a and b can be obtained in closed form by a straightforward example of maximum spacing estimation. Can't see any reason that applying thi... | How to check that a sample suits multi-dimensional uniform distribution?
For the 1D continuous uniform distribution U(a,b) the uniformly minimum variance unbiased (UMVU) estimates of a and b can be obtained in closed form by a straightforward example of maximum spacing est |
50,800 | How can I control the false positives rate? | Does this make sense: To me, mostly yes... although I think you might be doing something I don't expect (see below).
What is this method called and where can I find more about it: You are building up an empirical reference distribution through permutation of your genome labels. There may be fancier terms. I don't kno... | How can I control the false positives rate? | Does this make sense: To me, mostly yes... although I think you might be doing something I don't expect (see below).
What is this method called and where can I find more about it: You are building up | How can I control the false positives rate?
Does this make sense: To me, mostly yes... although I think you might be doing something I don't expect (see below).
What is this method called and where can I find more about it: You are building up an empirical reference distribution through permutation of your genome label... | How can I control the false positives rate?
Does this make sense: To me, mostly yes... although I think you might be doing something I don't expect (see below).
What is this method called and where can I find more about it: You are building up |
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