Id stringlengths 1 6 | PostTypeId stringclasses 7
values | AcceptedAnswerId stringlengths 1 6 ⌀ | ParentId stringlengths 1 6 ⌀ | Score stringlengths 1 4 | ViewCount stringlengths 1 7 ⌀ | Body stringlengths 0 38.7k | Title stringlengths 15 150 ⌀ | ContentLicense stringclasses 3
values | FavoriteCount stringclasses 3
values | CreationDate stringlengths 23 23 | LastActivityDate stringlengths 23 23 | LastEditDate stringlengths 23 23 ⌀ | LastEditorUserId stringlengths 1 6 ⌀ | OwnerUserId stringlengths 1 6 ⌀ | Tags list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2259 | 1 | 2265 | null | 5 | 3143 | Imagine that
- responses were collected on a 20 item scale which was designed to measure 4 factors with 5 items on each scale.
- participants were drawn from two groups (Group 1) and (Group 2) with sample size $n_1 = 150$ and $n_2 = 150$.
- a researcher wanted to assess the factor structure of the scale
Common s... | When is it acceptable to collapse across groups when performing a factor analysis? | CC BY-SA 2.5 | null | 2010-09-01T07:23:52.913 | 2010-09-01T11:01:01.643 | null | null | 183 | [
"factor-analysis",
"sampling"
] |
2260 | 2 | null | 2259 | 1 | null | It might be a little fly by night, but your theory may suggest whether the two groups have the same factor structure or not. If your theory suggests they do, and there is no reason to doubt the theory, I'd suggest you could go right ahead and trust that they have the same factor structure.
Your empirical assessment ... | null | CC BY-SA 2.5 | null | 2010-09-01T07:55:30.543 | 2010-09-01T07:55:30.543 | null | null | 196 | null |
2261 | 2 | null | 2259 | 2 | null | The approach you mention seems reasonable, but you'd have to take into account that you cannot see the total dataset as a single population. So theoretically, you should use any kind of method that can take differences between those groups into account, similar to using "group" as a random term in an ANOVA or GLM appro... | null | CC BY-SA 2.5 | null | 2010-09-01T08:44:54.663 | 2010-09-01T08:44:54.663 | null | null | 1124 | null |
2262 | 1 | 2263 | null | 0 | 5990 | I am currently into a situation that i don't really know how to solve by myself.
I need to calculate the AUC of each peak and then compare these areas in relation to each other. The problem is that the peaks are not completely separated and the only information i got is the mean and the SD of each peak.
Does anyone kno... | Area Under Curve (AUC) - given peak mean and standard deviation (SD) | CC BY-SA 2.5 | null | 2010-09-01T09:46:35.167 | 2010-09-01T14:52:06.747 | null | null | 1133 | [
"normal-distribution"
] |
2263 | 2 | null | 2262 | 3 | null | That really depends on the form and the height of the curve. If you assume the curves are all gaussian and you know the heights, then you can calculate the area under the curve by using the normal density function. In R this would become:
```
heights <- 1
avg <- 3
sdev <- 2
AUC <- heights/dnorm(avg,avg,sd) # the densi... | null | CC BY-SA 2.5 | null | 2010-09-01T10:10:54.380 | 2010-09-01T10:10:54.380 | null | null | 1124 | null |
2264 | 1 | null | null | 4 | 1327 | I've got product ratings for a few thousand products. The number of ratings for each product varies from zero to about fifty. I want to find the expected value of product rating for each product. If there are lots of ratings for the product I'd expect the expected value to be the average of the ratings for the product,... | Expected value of small sample | CC BY-SA 3.0 | null | 2010-09-01T10:20:34.940 | 2012-07-10T08:24:31.113 | 2012-07-10T01:01:21.760 | 9007 | 1134 | [
"expected-value"
] |
2265 | 2 | null | 2259 | 4 | null | There seems to be two cases to consider, depending on whether your scale was already validated using standard psychometric methods (from classical test or item response theory). In what follows, I will consider the first case where I assume preliminary studies have demonstrated construct validity and scores reliability... | null | CC BY-SA 2.5 | null | 2010-09-01T11:01:01.643 | 2010-09-01T11:01:01.643 | null | null | 930 | null |
2266 | 2 | null | 2264 | 3 | null | The "true" expected value cannot be calculated. You can estimate it using the mean of the ratings for each product, and get an idea about the position by calculating the 95% confidence interval (CI) on the mean.
This is done by
$CI \approx avg \pm 2 * \frac{SD}{\sqrt{n}}$
with n being the number of ratings, SD the stan... | null | CC BY-SA 3.0 | null | 2010-09-01T11:26:03.903 | 2012-07-10T08:24:31.113 | 2012-07-10T08:24:31.113 | 1124 | 1124 | null |
2267 | 2 | null | 73 | 3 | null | You can also take a look at [Task views](http://cran.r-project.org/web/views/) on CRAN and see if something suit your needs. I agree with @Jeromy for these must-have packages (for data manipulation and plotting).
| null | CC BY-SA 2.5 | null | 2010-09-01T11:31:31.833 | 2010-09-01T11:31:31.833 | null | null | 930 | null |
2268 | 2 | null | 2264 | 0 | null | I haven't looked into it much, but this article on [Bayesian rating systems](http://www.thebroth.com/blog/118/bayesian-rating) looks interesting.
| null | CC BY-SA 2.5 | null | 2010-09-01T11:57:18.720 | 2010-09-01T11:57:18.720 | null | null | 183 | null |
2269 | 1 | null | null | 3 | 1154 | How can I access tables created in SAS Enterprise Guide Client into SAS Enterprise Miner Client?
| Access tables created in SAS Enterprise Guide Client into SAS Enterprise Miner Client? | CC BY-SA 2.5 | null | 2010-09-01T12:07:28.173 | 2011-05-31T18:52:33.753 | null | null | 1135 | [
"sas"
] |
2270 | 2 | null | 1963 | 10 | null | I'll add an independent recommendation for Jeromy's blog post, and second the suggestions of James DeCoster's notes and the Borenstein textbook (propofols' no. 2).
At risk of indulging in self-promotion, I recently published a methods paper entitled [Getting Started with Meta-analysis](http://onlinelibrary.wiley.com/do... | null | CC BY-SA 2.5 | null | 2010-09-01T12:21:52.743 | 2010-09-01T12:21:52.743 | null | null | 266 | null |
2272 | 1 | 2287 | null | 313 | 184003 | Joris and Srikant's exchange [here](https://stats.stackexchange.com/questions/2182/can-you-explaining-why-statistical-tie-is-not-naively-when-p-1-p-2-2-moe/2242#2242) got me wondering (again) if my internal explanations for the difference between confidence intervals and credible intervals were the correct ones. How y... | What's the difference between a confidence interval and a credible interval? | CC BY-SA 4.0 | null | 2010-09-01T13:53:07.183 | 2021-12-23T14:34:02.923 | 2020-07-03T23:59:57.003 | 11887 | 71 | [
"bayesian",
"confidence-interval",
"frequentist",
"credible-interval",
"fiducial"
] |
2274 | 2 | null | 2262 | 1 | null | Given how your plot looks like, I would suggest rather to fit a mixture of gaussians and get their respective densities. Look at the [mclust](http://cran.r-project.org/web/packages/mclust/index.html) package; basically this is refered to model-based clustering (you are seeking groups of points belonging to a given dist... | null | CC BY-SA 2.5 | null | 2010-09-01T14:09:09.870 | 2010-09-01T14:09:09.870 | null | null | 930 | null |
2275 | 1 | 2285 | null | 7 | 795 | I do not study statistics but engineering, but this is a statistics question, and I hope you can lead me to what I need to learn to solve this problem.
I have this situation where I calculate probabilities of 1000's of things happening in like 30 days. If in 30 days I see what actually happened, how can I test to se... | How can I determine accuracy of past probability calculations? | CC BY-SA 2.5 | null | 2010-09-01T14:29:02.513 | 2010-09-02T00:12:45.650 | 2010-09-02T00:12:45.650 | 159 | 1137 | [
"probability"
] |
2277 | 2 | null | 2262 | 1 | null | It is critical to know how the peak heights and sds were calculated. (I take "mean" in the question to be a mistaken way of referring to a height. Without the heights, the problem is hopeless; it would be like requesting a formula for the area of a rectangle given only its width and location.)
One would expect, as Jo... | null | CC BY-SA 2.5 | null | 2010-09-01T14:52:06.747 | 2010-09-01T14:52:06.747 | null | null | 919 | null |
2278 | 2 | null | 423 | 93 | null | And another one from xkcd.
Title: Self-Description
The mouseover text:
>
The contents of any one panel are
dependent on the contents of every
panel including itself. The graph of
panel dependencies is complete and
bidirectional, and each node has a
loop. T... | null | CC BY-SA 3.0 | null | 2010-09-01T15:02:00.683 | 2016-10-09T15:32:00.433 | 2016-10-09T15:32:00.433 | -1 | 442 | null |
2279 | 2 | null | 2258 | 2 | null | Strictly speaking, this is trivial: the preimage of $(S_2, F_2)$ is all of $S_1$ (by definition), which is measurable (by definition).
Perhaps you want to conclude that the preimage of any measurable subset of $S_2$ is measurable: that is a nice property of a function. However, this conclusion is not true in general, ... | null | CC BY-SA 2.5 | null | 2010-09-01T15:07:38.893 | 2010-09-01T15:07:38.893 | null | null | 919 | null |
2280 | 2 | null | 2264 | 4 | null | Incorporating a prior is one way to 'make up' for small samples. Another is to use a mixed model, with an intercept for the mean structure and a random intercept for each product. The estimate of the population mean plus the predicted random effect (BLUP) then offers a form of shrinkage, where values for products wit... | null | CC BY-SA 2.5 | null | 2010-09-01T16:01:33.643 | 2010-09-01T23:12:40.680 | 2010-09-01T23:12:40.680 | 1107 | 1107 | null |
2281 | 2 | null | 2272 | 47 | null | My understanding is as follows:
Background
Suppose that you have some data $x$ and you are trying to estimate $\theta$. You have a data generating process that describes how $x$ is generated conditional on $\theta$. In other words you know the distribution of $x$ (say, $f(x|\theta)$.
Inference Problem
Your inference pr... | null | CC BY-SA 2.5 | null | 2010-09-01T16:01:43.313 | 2010-09-01T16:01:43.313 | null | null | null | null |
2282 | 1 | 2283 | null | -1 | 199 | Following [this question](https://stats.stackexchange.com/questions/1676/i-just-installed-the-latest-version-of-r-what-packages-should-i-obtain), I wish to have some way of counting how many times I am using a package in my daily work.
Is there a function/package to do that?
In case there isn't, how would you construct... | Counting how many times a package has been loaded in R? | CC BY-SA 2.5 | null | 2010-09-01T16:04:03.513 | 2013-09-06T09:38:35.813 | 2017-04-13T12:44:36.923 | -1 | 253 | [
"r"
] |
2283 | 2 | null | 2282 | 3 | null | Overload `library()` and `require()` so that they report what they do (whichever way: append to a text file, say) and have those replacement functions loaded first at startup.
| null | CC BY-SA 2.5 | null | 2010-09-01T16:13:47.113 | 2010-09-01T16:13:47.113 | null | null | 334 | null |
2284 | 2 | null | 2275 | 10 | null | In their classic book on the Federalist papers, Mosteller and Wallace argue for a log penalty function: you penalize yourself $-\log(p)$ when you predict an event with probability $p$ and it occurs; the penalty for it not occurring equals $-\log(1-p)$. Thus, the penalty is high when whatever happens is unexpected acco... | null | CC BY-SA 2.5 | null | 2010-09-01T16:49:30.997 | 2010-09-01T16:49:30.997 | null | null | 919 | null |
2285 | 2 | null | 2275 | 10 | null | What you're looking for are called Scoring Rules, which are ways of evaluating probabilistic forecasts. They were invented in the 1950s by weather forecasters, and there has been a been a bit of work on them in the statistics community, but I don't know of any books on the topic.
One thing you could do would be to bin ... | null | CC BY-SA 2.5 | null | 2010-09-01T17:30:32.223 | 2010-09-01T17:30:32.223 | null | null | 495 | null |
2286 | 2 | null | 1337 | 13 | null | Here's a groaner:
Q: What do you call 100 statisticians at a tea party?
A: A Z-Party.
| null | CC BY-SA 2.5 | null | 2010-09-01T18:23:56.687 | 2010-09-01T18:23:56.687 | null | null | 1118 | null |
2287 | 2 | null | 2272 | 425 | null | I agree completely with Srikant's explanation. To give a more heuristic spin on it:
Classical approaches generally posit that the world is one way (e.g., a parameter has one particular true value), and try to conduct experiments whose resulting conclusion -- no matter the true value of the parameter -- will be correct ... | null | CC BY-SA 3.0 | null | 2010-09-01T18:46:23.463 | 2012-11-18T21:29:20.383 | 2012-11-18T21:29:20.383 | 1122 | 1122 | null |
2288 | 2 | null | 2169 | 1 | null | It looks like I am probably stuck with a bootstrap. One interesting possibility here is to compute the 'exact bootstrap covariance', as outlined by [Hutson & Ernst](http://onlinelibrary.wiley.com/doi/10.1111/1467-9868.00221/abstract). Presumably the bootstrap covariance gives a good estimate of the standard error, asym... | null | CC BY-SA 2.5 | null | 2010-09-01T19:58:58.197 | 2010-09-01T19:58:58.197 | null | null | 795 | null |
2289 | 2 | null | 2244 | 5 | null | In an article in The American Statistician, Wolkewitz et al. use packages Epi, mvna, and survival. See Two Pitfalls in Survival Analyses of Time-Dependent Exposure: A Case Study in a Cohort of Oscar Nominees, v. 64 no. 3 (August 2010) pp 205-211. This exposition introduces multistate survival models and focuses on th... | null | CC BY-SA 2.5 | null | 2010-09-01T20:42:40.850 | 2010-09-01T20:42:40.850 | null | null | 919 | null |
2290 | 1 | 2310 | null | 8 | 7088 | This is a follow-up to the [repeated measures sample size](https://stats.stackexchange.com/questions/1818/how-to-determine-the-sample-size-needed-for-repeated-measurement-anova) question.
I am planning a repeated measures experiment. We record energy usage for 12 months, then give (a randomly assigned) half of the cust... | Determination of effect size for a repeated measures ANOVA power analysis | CC BY-SA 2.5 | null | 2010-09-01T21:06:34.860 | 2017-04-01T03:19:00.563 | 2017-04-13T12:44:33.550 | -1 | 743 | [
"r",
"repeated-measures",
"statistical-power"
] |
2291 | 1 | 2305 | null | 13 | 9603 | I'm working on a small (200M) corpus of text, which I want to explore with some cluster analysis. What books or articles on that subject would you recommend?
| Recommended books or articles as introduction to Cluster Analysis? | CC BY-SA 2.5 | null | 2010-09-01T23:57:06.760 | 2022-08-01T22:17:07.350 | 2010-09-17T20:23:04.700 | null | 138 | [
"machine-learning",
"references",
"clustering"
] |
2292 | 2 | null | 2237 | 2 | null | The ets() function uses maximum likelihood estimation. So it would be possible to obtain standard errors based on the Hessian matrix in the usual way. However, in forecasting, the value of the model parameters is usually of very limited interest -- what we care about are the forecasts and their variances.
I can't thin... | null | CC BY-SA 2.5 | null | 2010-09-02T00:30:48.977 | 2010-09-02T00:30:48.977 | null | null | 159 | null |
2293 | 2 | null | 1906 | 5 | null | NIPS: [http://nips.cc/](http://nips.cc/)
| null | CC BY-SA 2.5 | null | 2010-09-02T01:10:36.043 | 2010-09-02T01:10:36.043 | null | null | null | null |
2294 | 1 | 14507 | null | 6 | 445 | I have a four-state, discrete time Markov process with time-dependent transition matrices such that after a given time T the matrices become constant. The idea is people in a program leaving the program in a variety of ways. Everyone starts in state 1, and states 2, 3 and 4 are absorbing, but state 4 represents the fai... | Lumping in Markov process with absorbing states | CC BY-SA 2.5 | null | 2010-09-02T01:14:21.507 | 2011-11-17T09:30:40.370 | null | null | 1144 | [
"modeling",
"asymptotics",
"markov-process"
] |
2296 | 1 | 2297 | null | 8 | 752 | I am interested in fitting a factor analysis-like model on asset returns or other similar latent variable models. What are good papers to read on this topic? I am particularly interested in how to handle the fact that a factor analysis model is identical under a sign change for the "factor loadings".
| Papers on Bayesian factor analysis? | CC BY-SA 2.5 | null | 2010-09-02T02:00:45.543 | 2023-01-06T04:42:12.417 | null | null | 1146 | [
"bayesian",
"pca",
"factor-analysis"
] |
2297 | 2 | null | 2296 | 7 | null | Some references to help you out.
- Tipping, M. E. & Bishop, C. M.
Probabilistic principal component
analysis Journal of the Royal
Statistical Society (Series B),
1999, 21, 611-622
- Tom Minka. Automatic choice of
dimensionality for PCA. NIPS 2000
url:
http://research.microsoft.com/en-us/um/people/minka/papers/pca/
... | null | CC BY-SA 2.5 | null | 2010-09-02T02:19:37.283 | 2010-09-02T02:19:37.283 | null | null | 530 | null |
2298 | 1 | 2319 | null | 6 | 836 | This is somewhat vague, but suppose you have a black box function $f(x_1,x_2,\ldots,x_k)$, for which you have code, and you are interested in the behaviour of $f$ when the $x_i$ are i.i.d. standard Gaussian random variables. What are some good ways to visualize this function? To make it easier, we may assume that $k$ i... | Visualization of a multivariate function | CC BY-SA 2.5 | null | 2010-09-02T03:32:30.773 | 2010-09-02T21:56:32.277 | 2010-09-02T07:50:54.603 | null | 795 | [
"data-visualization",
"computational-statistics"
] |
2299 | 1 | 2303 | null | 24 | 666 | What broad methods are there to detect fraud, anomalies, fudging, etc. in scientific works produced by a third party? (I was motivated to ask this by the recent [Marc Hauser affair](http://en.wikipedia.org/wiki/Marc_Hauser#Scientific_misconduct).) Usually for election and accounting fraud, some variant of [Benford's La... | Statistical forensics: Benford and beyond | CC BY-SA 2.5 | null | 2010-09-02T04:01:56.450 | 2010-09-26T22:53:23.677 | 2010-09-18T21:53:38.603 | 930 | 795 | [
"meta-analysis",
"fraud-detection"
] |
2300 | 2 | null | 2296 | 2 | null | A decent overview of factor analysis is [Latent Variable Methods and Factor Analysis](http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470711108.html) by Bartholomew and Knott. They write about the interpretation of latent factors. This book is not as algorithmically-oriented as I would like, but their description o... | null | CC BY-SA 2.5 | null | 2010-09-02T04:13:24.343 | 2010-09-02T04:13:24.343 | null | null | 795 | null |
2301 | 2 | null | 2291 | 3 | null | Cluster Analysis by Brian S. Everitt is a nice book length applied treatment of Cluster Analysis.
| null | CC BY-SA 2.5 | null | 2010-09-02T04:23:58.130 | 2010-09-02T04:23:58.130 | null | null | 485 | null |
2302 | 2 | null | 2291 | 5 | null | This chapter of [Introduction to Data Mining](http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf) is available online and gives a nice overview.
| null | CC BY-SA 2.5 | null | 2010-09-02T05:24:13.440 | 2010-09-02T05:24:13.440 | null | null | 5 | null |
2303 | 2 | null | 2299 | 11 | null | Great Question!
In the scientific context there are various kinds of problematic reporting and problematic behaviour:
- Fraud: I'd define fraud as a deliberate intention on the part of the author or analyst to misrepresent the results and where the misrepresentation is of a sufficiently grave nature. The main example ... | null | CC BY-SA 2.5 | null | 2010-09-02T05:45:28.647 | 2010-09-02T05:45:28.647 | null | null | 183 | null |
2304 | 2 | null | 2298 | 3 | null | Just a thought, although I've never tried it.
- you could obtain a large number of values from the function across different parameter values
- take a tour of the resulting data in ggobi (check out Mat Kelcey's video)
| null | CC BY-SA 2.5 | null | 2010-09-02T06:33:11.917 | 2010-09-02T06:33:11.917 | null | null | 183 | null |
2305 | 2 | null | 2291 | 6 | null | It may be worth looking at M.W. Berry's books:
- Survey of Text Mining I: Clustering, Classification, and Retrieval (2003)
- Survey of Text Mining II: Clustering, Classification, and Retrieval (2008)
They consist of series of applied and review papers. The latest seems to be available as PDF at the following addre... | null | CC BY-SA 2.5 | null | 2010-09-02T10:25:32.673 | 2010-09-02T10:25:32.673 | 2017-04-13T12:44:52.277 | -1 | 930 | null |
2306 | 1 | 2307 | null | 92 | 48617 | I am getting a bit confused about feature selection and machine learning
and I was wondering if you could help me out. I have a microarray dataset that is
classified into two groups and has 1000s of features. My aim is to get a small number of genes (my features) (10-20) in a signature that I will in theory be able t... | Feature selection for "final" model when performing cross-validation in machine learning | CC BY-SA 2.5 | null | 2010-09-02T10:25:42.330 | 2022-04-23T14:22:01.647 | 2012-02-01T17:56:30.787 | 930 | 1150 | [
"machine-learning",
"classification",
"cross-validation",
"feature-selection",
"genetics"
] |
2307 | 2 | null | 2306 | 41 | null | Whether you use LOO or K-fold CV, you'll end up with different features since the cross-validation iteration must be the most outer loop, as you said. You can think of some kind of voting scheme which would rate the n-vectors of features you got from your LOO-CV (can't remember the paper but it is worth checking the wo... | null | CC BY-SA 4.0 | null | 2010-09-02T10:46:12.320 | 2020-10-19T04:09:43.253 | 2020-10-19T04:09:43.253 | 93018 | 930 | null |
2308 | 2 | null | 1980 | 10 | null | [Irreproducibility of NCI60 Predictors of Chemotherapy](http://bioinformatics.mdanderson.org/Supplements/ReproRsch-Chemo/)
This is a reproducible analysis showing the lack of reproducibility of a paper that has been in the news. A clinical trial based on the false conclusions of the irreproducible paper was suspended,... | null | CC BY-SA 2.5 | null | 2010-09-02T11:15:56.443 | 2010-09-02T14:57:59.267 | 2010-09-02T14:57:59.267 | 319 | 319 | null |
2309 | 2 | null | 2306 | 17 | null | To add to chl: When using support vector machines, a highly recommended penalization method is the elastic net. This method will shrink coefficients towards zero, and in theory retains the most stable coefficients in the model. Initially it was used in a regression framework, but it is easily extended for use with supp... | null | CC BY-SA 4.0 | null | 2010-09-02T11:29:47.973 | 2022-04-23T14:22:01.647 | 2022-04-23T14:22:01.647 | 79696 | 1124 | null |
2310 | 2 | null | 2290 | 3 | null | Assuming you are going to average the first 12 months to form a baseline measure and the second 12 months to form as a follow-up measure, your problem reduces to a repeated measures t-test.
G*Power
You might want to check out the following menu in G*Power 3:
`Tests - Means - Two Dependent Groups (matched pairs)`.
Use ... | null | CC BY-SA 3.0 | null | 2010-09-02T12:27:52.250 | 2017-04-01T03:19:00.563 | 2017-04-01T03:19:00.563 | 183 | 183 | null |
2312 | 2 | null | 2291 | 1 | null | Not specifically about text-mining, but I quite liked ["Exploratory Data Analysis with MATLAB"](http://rads.stackoverflow.com/amzn/click/1584883669) by Martinez and Martinez.
| null | CC BY-SA 2.5 | null | 2010-09-02T13:10:52.193 | 2010-09-02T13:10:52.193 | null | null | 582 | null |
2314 | 2 | null | 2306 | 10 | null | As step 6 (or 0) you run the feature detection algorithm on the entire data set.
The logic is the following: you have to think of cross-validation as a method for finding out the properties of the procedure you are using to select the features. It answers the question: "if I have some data and perform this procedure, t... | null | CC BY-SA 2.5 | null | 2010-09-02T15:56:37.563 | 2010-09-02T15:56:37.563 | null | null | 279 | null |
2315 | 1 | null | null | 3 | 660 | A previous user asked [this question](https://stats.stackexchange.com/questions/1266/a-non-parametric-repeated-measures-multi-way-anova-in-r) specifically for R. I'd like to know what, if any, other software can do this.
| What software allows non-parametric repeated-measures multi-way Anova? | CC BY-SA 2.5 | null | 2010-09-02T16:12:52.183 | 2010-09-03T17:53:09.490 | 2017-04-13T12:44:20.903 | -1 | 132 | [
"anova",
"nonparametric",
"software"
] |
2316 | 2 | null | 2264 | 0 | null | Ha! I've answered my own question. Simon Funk figured this out for the Netflix challenge [here](http://sifter.org/~simon/journal/20061211.html). See the paragraph commencing "However, even this isn't quite as simple as it appears". But I'm having difficulty proving it algebraically: maybe you guys would like to take th... | null | CC BY-SA 2.5 | null | 2010-09-02T16:58:19.700 | 2010-09-02T16:58:19.700 | null | null | 1134 | null |
2317 | 2 | null | 2306 | 45 | null | In principle:
Make your predictions using a single model trained on the entire dataset (so there is only one set of features). The cross-validation is only used to estimate the predictive performance of the single model trained on the whole dataset. It is VITAL in using cross-validation that in each fold you repeat t... | null | CC BY-SA 3.0 | null | 2010-09-02T17:53:16.097 | 2014-07-27T14:17:04.517 | 2014-07-27T14:17:04.517 | 2669 | 887 | null |
2318 | 2 | null | 2179 | 9 | null | Isabelle Guyon, André Elisseeff, "An Introduction to Variable and Feature Selection", JMLR, 3(Mar):1157-1182, 2003. [http://jmlr.csail.mit.edu/papers/v3/guyon03a.html](http://jmlr.csail.mit.edu/papers/v3/guyon03a.html)
is well worth reading, it will give a good overview of approaches and issues. The one thing I would ... | null | CC BY-SA 2.5 | null | 2010-09-02T18:07:50.913 | 2010-09-02T18:07:50.913 | null | null | 887 | null |
2319 | 2 | null | 2298 | 4 | null | Given that you are at the initial, exploratory stages of the analysis I would start simple. Consider sampling your inputs using a [Latin Hypercube](http://en.wikipedia.org/wiki/Latin_hypercube_sampling) strategy. Then, a tornado chart can be used to get a quick assessment of the multiple,one-way sensitivities f() has t... | null | CC BY-SA 2.5 | null | 2010-09-02T18:56:34.177 | 2010-09-02T18:56:34.177 | null | null | 1080 | null |
2320 | 2 | null | 2315 | 1 | null | This question was updated with a link to the previous question, at which point I realized that my response originally posted here pointing to the ez package in R was better left at the previous question.
| null | CC BY-SA 2.5 | null | 2010-09-02T19:22:14.867 | 2010-09-03T17:53:09.490 | 2010-09-03T17:53:09.490 | 364 | 364 | null |
2321 | 2 | null | 2298 | 0 | null | You could apply some sort of dimensionality reduction technique like principal components and plot the value of the function as you vary the first, second, third etc. principal components, holding all others fixed. This would show you how the function varies in the directions of the maximal variance of the inputs.
| null | CC BY-SA 2.5 | null | 2010-09-02T21:56:32.277 | 2010-09-02T21:56:32.277 | null | null | null | null |
2322 | 2 | null | 2306 | -1 | null | I'm not sure about classification problems, but in the case of feature selection for regression problems, Jun Shao showed [that Leave-One-Out CV is asymptotically inconsistent](http://www.jstor.org/pss/2290328), i.e. the probability of selecting the proper subset of features does not converge to 1 as the number of samp... | null | CC BY-SA 2.5 | null | 2010-09-02T23:05:55.530 | 2010-09-02T23:05:55.530 | null | null | 795 | null |
2323 | 1 | null | null | 6 | 373 | I think that dynamic pricing algorithms (used in aviation and ticketing industry) is very statistical based, anyone here has experience with those algorithms with references for it?
| What are good references for dynamic pricing? | CC BY-SA 3.0 | null | 2010-09-03T00:34:44.053 | 2021-05-03T15:31:58.607 | 2021-05-03T15:31:58.607 | 11887 | 1167 | [
"time-series",
"references",
"algorithms",
"operations-research"
] |
2324 | 2 | null | 2323 | 4 | null | This article is highly cited:
"Yield Management at American Airlines" by Barry C. Smith et al.
Links:
- JSTOR
- free PDF 1, broken at 06.09.12
- free PDF 2, broken at 02.01.18
- free PDF 3
| null | CC BY-SA 3.0 | null | 2010-09-03T02:15:18.283 | 2018-01-02T14:15:19.883 | 2018-01-02T14:15:19.883 | 187023 | 74 | null |
2325 | 2 | null | 1432 | 9 | null |
## On Pearsons residuals,
The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability. Therefore standardizing the residuals.
For large samples the standardized residuals should have a normal distribution.
From Menard... | null | CC BY-SA 2.5 | null | 2010-09-03T02:27:00.000 | 2010-09-03T02:27:00.000 | null | null | 10229 | null |
2326 | 1 | 2366 | null | 2 | 398 | I am looking at setting up an experiment concerning a hobby of mine, basically measuring a variety of parameters 'before' and 'after' and see which one, if any, gives the most reliable prediction of a final parameter i.e. do they have a linear relationship, etc. The object being to save some time and effort later not ... | Setting up experiment for statistical analysis | CC BY-SA 2.5 | null | 2010-09-03T03:25:40.293 | 2010-09-16T06:35:12.133 | 2010-09-16T06:35:12.133 | null | 1114 | [
"r",
"experiment-design"
] |
2327 | 2 | null | 1126 | 1 | null | In my opinion 16 are too many reasons, too fine of a specification and sort of overlap at times. Instead I would personally streamline into broad groups. We can classify study objectives in 3 main categories: single hypothesis testing, exploratory study and to predict.
| null | CC BY-SA 2.5 | null | 2010-09-03T03:49:48.030 | 2010-09-03T03:49:48.030 | null | null | 10229 | null |
2328 | 1 | 2332 | null | 14 | 3199 | What should be the ratio of number of observations and number of variables?
How to detect overfitting in the neural network model and what are the ways to avoid overfitting?
If I want to perform classification with Neural Network, should the classes have equal frequency?
Please help me out.
| How to perform Neural Network modelling effectively? | CC BY-SA 2.5 | null | 2010-09-03T04:53:37.240 | 2013-12-04T00:23:59.377 | 2010-09-03T04:59:10.847 | 183 | 861 | [
"neural-networks"
] |
2329 | 2 | null | 534 | 3 | null | Suppose we think the factor A is the cause of the phenomenon B. Then we try to vary it to see whether B changes.
If B doesn't change and if we can assume that everything else unchanged, strong evidence that A is not the cause of B.
If B does change, we can't conclude that A is the cause because the change of A might ... | null | CC BY-SA 2.5 | null | 2010-09-03T06:03:54.097 | 2010-09-03T06:03:54.097 | null | null | null | null |
2330 | 2 | null | 852 | 9 | null | In the usual case with a log variable, the model is
\begin{align}
\log(y) &= a + b\log(x) + \varepsilon\newline
\text{or}\quad y &= e^a x^b e^\varepsilon,
\end{align}
where $\varepsilon\sim\text{N}(0,\sigma^2)$ and $b$ is the elasticity.
In the situation you mention,
\begin{align}
y &= \exp[a + b\log(x)] + \varepsilon... | null | CC BY-SA 2.5 | null | 2010-09-03T06:56:22.773 | 2010-09-03T06:56:22.773 | null | null | 159 | null |
2331 | 4 | null | null | 0 | null | Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`. | null | CC BY-SA 3.0 | null | 2010-09-03T07:25:03.153 | 2016-01-15T23:25:24.240 | 2016-01-15T23:25:24.240 | 7290 | 183 | null |
2332 | 2 | null | 2328 | 26 | null | The advice I would give is as follows:
- Exhaust the possibilities of linear models (e.g. logistic regression) before going on to neural nets, especially if you have many features and not too many observations. For many problems a Neural Net does not out-perform simple linear classifiers, and the only way to find out... | null | CC BY-SA 3.0 | null | 2010-09-03T08:03:15.777 | 2013-12-04T00:23:59.377 | 2013-12-04T00:23:59.377 | 9007 | 887 | null |
2333 | 2 | null | 58 | 11 | null | Back-propogation is a way of working out the derivative of the error function with respect to the weights, so that the model can be trained by gradient descent optimisation methods - it is basically just the application of the "chain rule". There isn't really much more to it than that, so if you are comfortable with c... | null | CC BY-SA 2.5 | null | 2010-09-03T08:28:20.503 | 2010-09-03T08:28:20.503 | null | null | 887 | null |
2334 | 2 | null | 181 | 50 | null | I am working on an empirical study of this at the moment (approching a processor-century of simulations on our HPC facility!). My advice would be to use a "large" network and regularisation, if you use regularisation then the network architecture becomes less important (provided it is large enough to represent the und... | null | CC BY-SA 2.5 | null | 2010-09-03T08:40:44.130 | 2010-09-03T08:40:44.130 | null | null | 887 | null |
2335 | 1 | 2342 | null | 10 | 4030 | I'm trying to interpret the following type of logistic model:
```
mdl <- glm(c(suc,fail) ~ fac1 + fac2, data=df, family=binomial)
```
Is the output of `predict(mdl)` the expected odds of success for each data point? Is there a simple way to tabulate the odds for each factor level of the model, rather than all the data... | Output of logistic model in R | CC BY-SA 2.5 | null | 2010-09-03T08:53:27.510 | 2010-09-03T15:16:08.050 | 2010-09-03T09:23:26.890 | 229 | 229 | [
"r",
"logistic",
"generalized-linear-model"
] |
2336 | 2 | null | 2131 | 1 | null | I would advise using a different value of the regularisation parameter C for examples of the positive class and examples of the negative class (many SVM packages support this, and in any case it is easily implemented). Then use e.g. cross-validation to find good values of the two regularisation parameters.
It can be... | null | CC BY-SA 2.5 | null | 2010-09-03T09:12:37.553 | 2010-09-03T09:12:37.553 | null | null | 887 | null |
2337 | 1 | 2340 | null | 3 | 3831 | I have a data-set consisting of N p-dimensional observations (all quantitative variables). I want to apply a hierarchical clustering algorithm to those data. As explained on page 505 in [Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/), when using weighted average to combine the dist... | How to standardize a data-set | CC BY-SA 2.5 | null | 2010-09-03T09:38:35.287 | 2010-09-16T06:35:32.243 | 2010-09-16T06:35:32.243 | null | 977 | [
"standardization"
] |
2338 | 2 | null | 1266 | 8 | null | The [ez](http://cran.r-project.org/web/packages/ez/index.html) package, of which I am the author, has a function called ezPerm() which computes a permutation test, but probably doesn't do interactions properly (the documentation admits as much). The latest version has a function called ezBoot(), which lets you do boots... | null | CC BY-SA 2.5 | null | 2010-09-03T09:41:58.997 | 2010-09-03T09:41:58.997 | null | null | 364 | null |
2340 | 2 | null | 2337 | 2 | null | I'd just say: be careful with that. Standarization is needed only when some variable(s) dominates the dissimilarity score just because it is expressed in "smaller units"; let's say that you have a variable that is truly equal for all elements, but there is some, very small variability due to the measurement error. Now ... | null | CC BY-SA 2.5 | null | 2010-09-03T11:04:29.063 | 2010-09-03T11:04:29.063 | null | null | null | null |
2342 | 2 | null | 2335 | 14 | null | The help pages for
```
predict.glm
```
state: "Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and ‘type = "response"’ gives the predicted probabilities". So, `predict(mdl)` returns the log(odds), and using "type = "response" returns the predicted probab... | null | CC BY-SA 2.5 | null | 2010-09-03T13:21:10.640 | 2010-09-03T15:16:08.050 | 2010-09-03T15:16:08.050 | 307 | 307 | null |
2343 | 1 | null | null | 3 | 421 | I am trying to develop some algorithm to compute probabilities in multi-type branching trees, and I doubt I am doing this right...
Let us consider a multi-type branching process with two types, denoted by 0 and 1. The process starts in state 0 with probability 1, so that the root vertex of any tree generated by this pr... | Trees generated by multi-type branching processes in n steps | CC BY-SA 4.0 | null | 2010-09-03T13:21:55.897 | 2022-06-24T22:03:46.603 | 2022-06-24T22:03:46.603 | 79696 | 1185 | [
"probability",
"algorithms",
"stochastic-processes"
] |
2344 | 1 | 2346 | null | 81 | 65614 | I am using the random forest algorithm as a robust classifier of two groups in a microarray study with 1000s of features.
- What is the best way to present the random forest so that there is enough information to make it
reproducible in a paper?
- Is there a plot method in R to actually plot the tree, if there are ... | Best way to present a random forest in a publication? | CC BY-SA 3.0 | null | 2010-09-03T13:50:51.707 | 2018-01-11T10:22:37.090 | 2018-01-11T10:22:37.090 | 128677 | 1150 | [
"r",
"machine-learning",
"classification",
"random-forest",
"microarray"
] |
2345 | 2 | null | 2343 | 4 | null | I think you explained well why the probability of the given tree is 0.5 if its topology does not count. Looking at the formula (2) superficially, I find it hard to imagine the definition of isomorphism under which it would work (only leaves can be rearranged?), though perhaps the trick is in finding the right definitio... | null | CC BY-SA 2.5 | null | 2010-09-03T14:19:30.763 | 2010-09-03T14:19:30.763 | null | null | 279 | null |
2346 | 2 | null | 2344 | 52 | null | Regarding making it reproducible, the best way is to provide reproducible research (i.e. code and data) along with the paper. Make it available on your website, or on a hosting site (like github).
Regarding visualization, Leo Breiman has done some interesting work on this (see [his homepage](http://www.stat.berkeley.e... | null | CC BY-SA 2.5 | null | 2010-09-03T14:32:35.293 | 2010-09-03T15:30:56.143 | 2010-09-03T15:30:56.143 | 5 | 5 | null |
2347 | 2 | null | 3 | 14 | null | There are also those projects initiated by the FSF or redistributed under GNU General Public License, like:
- PSPP, which aims to be a free alternative to SPSS
- GRETL, mostly dedicated to regression and econometrics
There is even applications that were released just as a companion software for a textbook, like [JM... | null | CC BY-SA 2.5 | null | 2010-09-03T14:42:15.677 | 2010-09-03T14:42:15.677 | null | null | 930 | null |
2348 | 1 | null | null | 19 | 1775 | I was reading [Christian Robert's Blog](http://xianblog.wordpress.com/2010/09/02/random-dive-mh) today and quite liked the new Metropolis-Hastings algorithm he was discussing. It seemed simple and easy to implement.
Whenever I code up MCMC, I tend to stick with very basic MH algorithms, such as independent moves or ran... | Metropolis-Hastings algorithms used in practice | CC BY-SA 2.5 | null | 2010-09-03T15:02:58.163 | 2010-10-05T12:11:53.590 | 2010-09-04T10:49:24.537 | 8 | 8 | [
"markov-chain-montecarlo",
"metropolis-hastings"
] |
2349 | 2 | null | 2269 | 2 | null | Obviously there is probably a better solution available than what I'm about to say - especially since these are both SAS products. However but I think it bares saying that if all else fails, when your data is in a tabular structure, you can almost always export the data as a deliminated text file (e.g. csv) and import... | null | CC BY-SA 2.5 | null | 2010-09-03T15:51:04.217 | 2010-09-03T15:51:04.217 | null | null | 196 | null |
2350 | 1 | 2359 | null | 15 | 8162 | I am applying a random forest algorithm as a classifier on a microarray dataset which are split into two known groups with 1000s of features. After the initial run I look at the importance of the features and run the tree algorithm again with the 5, 10 and 20 most important features. I find that for all features, top... | Why does the random forest OOB estimate of error improve when the number of features selected are decreased? | CC BY-SA 2.5 | null | 2010-09-03T15:55:37.100 | 2017-07-04T14:22:58.973 | null | null | 1150 | [
"r",
"machine-learning",
"classification",
"random-forest"
] |
2351 | 2 | null | 2348 | 2 | null | Hybrid Monte Carlo is the standard algorithm used for neural networks. Gibbs sampling for Gaussian process classification (when not using a deterministic approximation instead).
| null | CC BY-SA 2.5 | null | 2010-09-03T16:00:07.917 | 2010-09-03T16:00:07.917 | null | null | 887 | null |
2352 | 1 | null | null | 25 | 3046 | In his paper [Linear Model Selection by Cross-Validation](http://www.jstor.org/pss/2290328), Jun Shao shows that for the problem of variable selection in multivariate linear regression, the method of leave-one-out cross validation (LOOCV) is 'asymptotically inconsistent'. In plain English, it tends to select models wit... | When are Shao's results on leave-one-out cross-validation applicable? | CC BY-SA 3.0 | null | 2010-09-03T16:15:14.543 | 2020-02-27T20:39:06.940 | 2016-04-26T04:00:02.200 | 795 | 795 | [
"classification",
"model-selection",
"cross-validation"
] |
2353 | 2 | null | 3 | 19 | null | This may get downvoted to oblivion, but I happily used the Matlab clone [Octave](http://www.gnu.org/software/octave/) for many years. There are fairly good libraries in octave forge for generation of random variables from different distributions, statistical tests, etc, though clearly it is dwarfed by R. One possible a... | null | CC BY-SA 2.5 | null | 2010-09-03T16:27:56.423 | 2010-09-03T16:27:56.423 | null | null | 795 | null |
2354 | 2 | null | 2170 | 1 | null | Thanks again for the response, and any other responses in the future will be much appreciated. I think I personally prefer using exploratory tools to identify the relationships, especially since the original researcher did not give any real reason why a curvilinear relationship would exist theoretically. Although explo... | null | CC BY-SA 2.5 | null | 2010-09-03T17:57:15.193 | 2010-09-03T17:57:15.193 | null | null | 1036 | null |
2355 | 2 | null | 2352 | 6 | null | I would say: everywhere, but I haven't seen a strict proof of it. The intuition behind is such that when doing CV one must hold a balance between train large enough to build sensible model and test large enough so it would be a sensible benchmark.
When dealing with thousands of pretty homogeneous objects, picking one i... | null | CC BY-SA 2.5 | null | 2010-09-03T18:14:13.153 | 2010-09-03T18:14:13.153 | null | null | null | null |
2356 | 1 | null | null | 101 | 13975 | A recent question on the difference between confidence and credible intervals led me to start re-reading Edwin Jaynes' article on that topic:
Jaynes, E. T., 1976. `Confidence Intervals vs Bayesian Intervals,' in Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, W. L. Harper ... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | CC BY-SA 3.0 | null | 2010-09-03T18:23:44.087 | 2022-07-03T15:40:27.170 | 2016-07-24T17:31:36.353 | 103338 | 887 | [
"bayesian",
"confidence-interval"
] |
2357 | 2 | null | 1668 | 1 | null | As a computer engineer coming to data analysis myself, a really readable book that covers things from a pretty unintimidating & readable perspective (at the cost of not covering as much as any of the other books suggested here) was Programming Collective Intelligence by Toby Segaran. I found it a lot more approachable ... | null | CC BY-SA 2.5 | null | 2010-09-03T18:51:15.067 | 2010-09-03T18:51:15.067 | null | null | 1076 | null |
2358 | 1 | 2360 | null | 90 | 178893 | Are multiple and multivariate regression really different? What is a variate anyways?
| Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | CC BY-SA 3.0 | null | 2010-09-03T18:54:17.230 | 2023-04-17T13:41:02.513 | 2015-10-28T09:32:08.590 | 28666 | 74 | [
"regression",
"multiple-regression",
"terminology",
"multivariate-regression"
] |
2359 | 2 | null | 2350 | 16 | null | This is feature selection overfit and this is pretty known -- see [Ambroise & McLachlan 2002](http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=124442&tool=pmcentrez&rendertype=abstract).
The problem is based on the facts that RF is too smart and number of objects is too small. In the latter case, it is genera... | null | CC BY-SA 2.5 | null | 2010-09-03T19:00:15.123 | 2010-09-03T19:00:15.123 | null | null | null | null |
2360 | 2 | null | 2358 | 67 | null | Very quickly, I would say: 'multiple' applies to the number of predictors that enter the model (or equivalently the design matrix) with a single outcome (Y response), while 'multivariate' refers to a matrix of response vectors. Cannot remember the author who starts its introductory section on multivariate modeling with... | null | CC BY-SA 2.5 | null | 2010-09-03T19:03:07.687 | 2010-09-19T09:32:42.730 | 2010-09-19T09:32:42.730 | 930 | 930 | null |
2361 | 2 | null | 1668 | 3 | null | Here is a very nice book from James E. Gentle, [Computational Statistics](http://www.springer.com/statistics/computanional+statistics/book/978-0-387-98143-7) (Springer, 2009), which covers both computational and statistical aspects of data analysis. Gentle also authored other great books, check his publications.
Anothe... | null | CC BY-SA 2.5 | null | 2010-09-03T19:22:30.447 | 2010-09-03T19:22:30.447 | null | null | 930 | null |
2362 | 2 | null | 2344 | 19 | null |
- As Shane wrote; make it reproducible research + include random seeds, because RF is stochastic.
- First of all, plotting single trees forming RF is nonsense; this is an ensemble classifier, it makes sense only as a whole. But even plotting the whole forest is nonsense -- it is a black-box classifier, so it is not i... | null | CC BY-SA 2.5 | null | 2010-09-03T19:22:56.617 | 2010-09-03T19:22:56.617 | null | null | null | null |
2363 | 2 | null | 2358 | 70 | null | Here are two closely related examples which illustrate the ideas. The examples are somewhat US centric but the ideas can be extrapolated to other countries.
Example 1
Suppose that a university wishes to refine its admission criteria so that they admit 'better' students. Also, suppose that a student's grade Point Averag... | null | CC BY-SA 2.5 | null | 2010-09-03T19:27:20.703 | 2010-09-03T19:27:20.703 | null | null | null | null |
2364 | 2 | null | 1668 | 1 | null | CRAN has several good examples of books pertaining to statistical programming. Some of them will not pertain to machine learning and MCMC, but each entry is annotated, so you should have a rough idea of what each book contains to dive a bit further.
[http://www.r-project.org/doc/bib/R-books.html](http://www.r-project.o... | null | CC BY-SA 2.5 | null | 2010-09-03T19:38:45.930 | 2010-09-03T19:38:45.930 | null | null | 1118 | null |
2365 | 2 | null | 2356 | 12 | null | The problem starts with your sentence :
>
Examples based on incorrect prior
assumptions are not acceptable as they
say nothing about the internal
consistency of the different
approaches.
Yeah well, how do you know your prior is correct?
Take the case of Bayesian inference in phylogeny. The probability of at... | null | CC BY-SA 2.5 | null | 2010-09-03T20:24:52.440 | 2010-09-03T23:00:59.537 | 2010-09-03T23:00:59.537 | 1124 | 1124 | null |
2366 | 2 | null | 2326 | 4 | null | Actually, people get paid big money for statistical guidance through experiments... If you're not too sure about it, I'd also advise to consult a statistician. An internet forum is not the best aid for complex analyses. Much of what is possible depends on the structure within the dataset: How are the variables distribu... | null | CC BY-SA 2.5 | null | 2010-09-03T21:16:13.070 | 2010-09-03T21:16:13.070 | null | null | 1124 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.