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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
966 | 2 | null | 964 | 3 | null | This is often used for a price to return transformation based on assuming continuously compounded returns. The Campbell, Lo, and MacKinlay book (Econometrics of Financial Markets, 1997) lays it out quite nicely:
Define r_t as the log of gross returns 1 + R_t:
```
r_t := log(1 + R_t)
```
which is the same as the log o... | null | CC BY-SA 2.5 | null | 2010-07-29T10:22:37.673 | 2010-07-29T10:22:37.673 | null | null | 334 | null |
967 | 2 | null | 726 | 26 | null | >
I keep saying that the sexy job in the next 10 years will be statisticians. And I'm not kidding.
[Hal Varian](http://en.wikipedia.org/wiki/Hal_Varian)
| null | CC BY-SA 3.0 | null | 2010-07-29T10:50:53.227 | 2011-08-15T05:59:45.090 | 2011-08-15T05:59:45.090 | 4479 | 215 | null |
968 | 2 | null | 726 | 108 | null | >
All generalizations are false,
including this one.
Mark Twain
| null | CC BY-SA 2.5 | null | 2010-07-29T10:57:18.647 | 2010-07-29T10:57:18.647 | null | null | 1356 | null |
969 | 2 | null | 726 | 18 | null | >
An ecologist is a statistician who likes to be outside.
-- apparently a good friend of [Murray Cooper](http://r.789695.n4.nabble.com/Why-software-fails-in-scientific-research-td1573062.html#a2275423).
| null | CC BY-SA 2.5 | null | 2010-07-29T11:25:13.733 | 2010-12-03T04:04:09.337 | 2010-12-03T04:04:09.337 | 795 | 144 | null |
970 | 2 | null | 951 | 1 | null | In general there is none. A Poisson process has inter-arrival times that are exponentially distributed, which does not have heavy tails.
| null | CC BY-SA 2.5 | null | 2010-07-29T11:41:41.960 | 2010-07-29T11:41:41.960 | null | null | 247 | null |
971 | 2 | null | 961 | 13 | null | At first I think that the Fisher test is used correctly.
Count data are better handled using log-linear models (not logit, to ensure that the fitted values are bounded below). In R you can specify `family=poisson` (which sets errors = Poisson and link = log). The log link ensures that all the fitted values are positive... | null | CC BY-SA 3.0 | null | 2010-07-29T11:41:49.370 | 2015-01-31T08:17:04.373 | 2015-01-31T08:17:04.373 | 339 | 339 | null |
972 | 2 | null | 602 | 1 | null | There are no general heuristics, you should make a grid search, especially since the value of nu must be between 0-1.
| null | CC BY-SA 2.5 | null | 2010-07-29T11:43:54.703 | 2010-07-29T11:43:54.703 | null | null | 566 | null |
973 | 1 | 1020 | null | 37 | 19205 | What are the freely available data set for classification with more than 1000 features (or sample points if it contains curves)?
There is already a community wiki about free data sets:
[Locating freely available data samples](https://stats.stackexchange.com/questions/7/locating-freely-available-data-samples/)
But here... | Free data set for very high dimensional classification | CC BY-SA 2.5 | null | 2010-07-29T12:02:28.347 | 2013-07-03T01:34:41.217 | 2017-04-13T12:44:21.160 | -1 | 223 | [
"machine-learning",
"classification",
"dataset",
"large-data"
] |
975 | 2 | null | 927 | 5 | null | Another good podcast is [In our time](http://www.bbc.co.uk/radio4/features/in-our-time/) by the BBC. It's a weekly podcast (off air for the summer) that deals with topics in History, Religion and Science. I would say that about 1 in 12 podcasts deal with Mathematics and Statistics. Take a look at the podcast archive fo... | null | CC BY-SA 2.5 | null | 2010-07-29T12:44:50.820 | 2010-07-29T12:44:50.820 | null | null | 8 | null |
977 | 1 | 978 | null | 4 | 1562 | When we are monitoring movements of structures we normally install monitoring points onto the structure before we do any work which might cause movement. This gives us chance to take a few readings before we start doing the work to 'baseline' the readings.
Quite often the data is quite variable (the variations in the ... | How many measurements are needed to 'baseline' a measurement? | CC BY-SA 2.5 | null | 2010-07-29T13:43:53.010 | 2010-07-30T15:37:06.190 | null | null | 210 | [
"variance",
"measurement"
] |
978 | 2 | null | 977 | 5 | null | I think you should look at [power calculations](http://en.wikipedia.org/wiki/Statistical_power). These are often used to decide the sample size of survey or clinical trial. Taken from wikipedia:
>
A priori power analysis is conducted
prior to the research study, and is
typically used to determine an
appropriate ... | null | CC BY-SA 2.5 | null | 2010-07-29T13:51:07.953 | 2010-07-29T13:51:07.953 | null | null | 8 | null |
980 | 1 | null | null | 13 | 38313 | I haven't studied statistics for over 10 years (and then just a basic course), so maybe my question is a bit hard to understand.
Anyway, what I want to do is reduce the number of data points in a series. The x-axis is number of milliseconds since start of measurement and the y-axis is the reading for that point.
Often... | How do I reduce the number of data points in a series? | CC BY-SA 2.5 | null | 2010-07-29T14:04:20.853 | 2018-11-16T16:36:41.907 | 2010-07-31T00:49:57.463 | 159 | null | [
"data-visualization"
] |
981 | 2 | null | 980 | 11 | null | You have two problems: too many points and how to smooth over the remaining points.
Thinning your sample
If you have too many observations arriving in real time, you could always use [simple random sampling](http://en.wikipedia.org/wiki/Simple_random_sample) to thin your sample. Note, for this too be true, the number o... | null | CC BY-SA 4.0 | null | 2010-07-29T14:15:09.800 | 2018-11-16T16:36:41.907 | 2018-11-16T16:36:41.907 | 78863 | 8 | null |
982 | 2 | null | 980 | 10 | null | Well, I think the word you're looking for is "sampling," but I'm not sure why you want to do it. Thousands of data points isn't very many. Or are you looking just to plot a smaller number of equally-spaced points? That's usually called "binning."
Is your goal to generate a visualization? In that case, you might want t... | null | CC BY-SA 2.5 | null | 2010-07-29T14:18:02.383 | 2010-07-29T14:18:02.383 | null | null | 6 | null |
983 | 2 | null | 977 | 3 | null | It really depends on the amount of variance relative to the size of a measurement that you care about. If you need to be able to tell the difference between a mean of 2 and a mean of 0, and your data look like this:
```
-4.4 3.8 -2.0 -5.1 0.2 7.1 0.9 -5.4 2.8 0.5
```
Then you're going to need a lot more data! But if y... | null | CC BY-SA 2.5 | null | 2010-07-29T14:26:53.593 | 2010-07-29T14:26:53.593 | null | null | 6 | null |
984 | 2 | null | 980 | 1 | null | You're not providing enough information. Why do you want to reduce the data points. A few thousand is nothing these days.
Given that you want the same result each time you view the same data perhaps you want to simply bin averages. You have variable spacing on your x-axis. Maybe you're trying to make that consisten... | null | CC BY-SA 2.5 | null | 2010-07-29T14:29:16.240 | 2010-07-29T14:29:16.240 | null | null | 601 | null |
986 | 2 | null | 961 | 4 | null | You can use multinom from nnet package for multinomial regression.
Post-hoc tests you can use linearHypothesis from car package.
You can conduct test of independence using linearHypothesis (Wald test) or anova (LR test).
| null | CC BY-SA 2.5 | null | 2010-07-29T15:20:56.393 | 2010-07-29T15:20:56.393 | null | null | 419 | null |
990 | 2 | null | 726 | 7 | null | >
You may be too vague to be wrong and
that's really bad cause that's just
obscuring the issue.
Bruce Sterling
| null | CC BY-SA 2.5 | null | 2010-07-29T15:43:35.187 | 2010-07-29T15:43:35.187 | null | null | 3807 | null |
991 | 2 | null | 964 | 1 | null | usually, you plot such a series to check the extend to which it exhibits heteroskedasticity.
Depending on the answer, you may have to model the residuals, even if you are only interested in the mean.
On the top of my head, this article is a practical example:
[http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBIQFj... | null | CC BY-SA 2.5 | null | 2010-07-29T15:45:39.227 | 2010-07-29T15:45:39.227 | null | null | 603 | null |
992 | 2 | null | 913 | 1 | null | Try a bivariate robust regression
(see [http://cran.r-project.org/web/packages/rrcov/vignettes/rrcov.pdf](http://cran.r-project.org/web/packages/rrcov/vignettes/rrcov.pdf) for an intro).
If your data points are all positive, you might want to try to regress log(y) on log(x).
Note that log() is not a substitute for a r... | null | CC BY-SA 2.5 | null | 2010-07-29T15:50:24.717 | 2010-07-29T15:50:24.717 | null | null | 603 | null |
993 | 2 | null | 924 | 6 | null | A (two-sided) Fisher's Exact test gives p-value = 0.092284.
```
function p = fexact(k, x, m, n)
%FEXACT Fisher's Exact test.
% Y = FEXACT(K, X, M, N) calculates the P-value for Fisher's
% Exact Test.
% K, X, M and N must be nonnegative integer vectors of the same
% length. The following must also hold:
% X <... | null | CC BY-SA 2.5 | null | 2010-07-29T15:57:21.187 | 2010-07-29T16:28:09.103 | 2010-07-29T16:28:09.103 | 506 | 506 | null |
994 | 2 | null | 421 | 8 | null | [The Drunkard's Walk: How Randomness Rules Our Lives](http://rads.stackoverflow.com/amzn/click/0713999225) by Leonard Mlodinow is an excellent book for laypeople. Enjoyable and educational.
It might not be a textbook, but it makes you think about the world in the right way.
| null | CC BY-SA 2.5 | null | 2010-07-29T16:00:17.130 | 2010-07-29T16:00:17.130 | null | null | null | null |
995 | 2 | null | 614 | 4 | null | Collaborative Statistics is CC BY: [http://cnx.org/content/col10522/latest/](http://cnx.org/content/col10522/latest/)
| null | CC BY-SA 2.5 | null | 2010-07-29T16:02:18.907 | 2010-07-29T16:02:18.907 | null | null | null | null |
996 | 2 | null | 213 | 20 | null | You can find a pedagogical summary of the various methods available in [(1)](http://download.springer.com/static/pdf/216/chp%253A10.1007%252F978-3-642-35494-6_4.pdf?auth66=1386938034_5f6480148bf543fd3993a8b2ff1ea06a&ext=.pdf)
For some --recent-- numerical comparisons of the various methods listed there, you can check
... | null | CC BY-SA 3.0 | null | 2010-07-29T16:13:41.440 | 2013-12-11T18:58:18.030 | 2013-12-11T18:58:18.030 | 603 | 603 | null |
997 | 2 | null | 924 | 10 | null | The huge denominators throw off one's intuition. Since the sample sizes are identical, and the proportions low, the problem can be recast: 13 events occurred, and were expected (by null hypothesis) to occur equally in both groups. In fact the split was 3 in one group and 10 in the other. How rare is that? The binomial ... | null | CC BY-SA 2.5 | null | 2010-07-29T16:50:11.297 | 2010-08-22T01:43:00.833 | 2010-08-22T01:43:00.833 | 25 | 25 | null |
998 | 2 | null | 924 | 0 | null | In addition to the other answers:
If you have 1,000,000 observations and when your event comes up only a few times, you are likely to want to look at a lot of different events.
If you look at 100 different events you will run into problems if you work with p<0.05 as criteria for significance.
| null | CC BY-SA 2.5 | null | 2010-07-29T18:06:31.570 | 2010-07-29T18:06:31.570 | null | null | 3807 | null |
999 | 2 | null | 138 | 4 | null | If you are coming from a SAS or SPSS background, check out:
[http://sites.google.com/site/r4statistics/](http://sites.google.com/site/r4statistics/)
This is the companion site to the book, R for SAS and SPSS Users by Robert Muenchen and a free version of the book can be found here.
| null | CC BY-SA 2.5 | null | 2010-07-29T18:33:43.370 | 2010-07-29T18:33:43.370 | null | null | null | null |
1000 | 2 | null | 951 | 4 | null | Well, if you have a point process that you try modeling as a Poisson process, and find it has heavy tails, there are several possibilities. What are the key assumptions for a Poisson Process:
-There is a constant rate function
-Events are memoryless, that is P(E in (t,t+d)) is independent of t and when other events ar... | null | CC BY-SA 2.5 | null | 2010-07-29T18:39:45.200 | 2010-07-29T18:39:45.200 | null | null | 549 | null |
1001 | 1 | 3078 | null | 5 | 2970 | I have distributions from two different data sets and I would like to
measure how similar their distributions (in terms of their bin
frequencies) are. In other words, I am not interested in the correlation of
data point sequences but rather in the their distributional properties with respect to similarity. Currently I... | Is Spearman's correlation coefficient usable to compare distributions? | CC BY-SA 3.0 | null | 2010-07-29T18:46:47.247 | 2022-04-17T17:42:24.263 | 2012-01-18T13:23:58.143 | null | 608 | [
"distributions",
"spearman-rho",
"paired-data"
] |
1003 | 2 | null | 614 | 13 | null | Try IPSUR, Introduction to Probability and Statistics Using R by G. Jay Kerns. It's "free, in the GNU sense of the word".
[http://ipsur.r-forge.r-project.org/book/](http://ipsur.r-forge.r-project.org/book/)
It's definitely open source - on the download page you can download the LaTeX source or the lyx source used to g... | null | CC BY-SA 3.0 | null | 2010-07-29T18:59:01.417 | 2016-10-06T19:36:07.787 | 2016-10-06T19:36:07.787 | 122650 | 36 | null |
1004 | 2 | null | 1001 | 10 | null | Rather use [Kolmogorov–Smirnov test](http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test), which is exactly what you need. R function `ks.test` implements it.
Also check [this question](https://stats.stackexchange.com/questions/411/motivation-for-kolmogorov-distance-between-distributions).
| null | CC BY-SA 2.5 | null | 2010-07-29T18:59:32.213 | 2010-07-29T19:14:52.727 | 2017-04-13T12:44:33.237 | -1 | null | null |
1005 | 2 | null | 138 | 11 | null | Try IPSUR, Introduction to Probability and Statistics Using R. It's a free book, free in the GNU sense of the word.
[http://ipsur.r-forge.r-project.org/book/index.php](http://ipsur.r-forge.r-project.org/book/index.php)
It's definitely open source - on the download page you can download the LaTeX source or the lyx sour... | null | CC BY-SA 2.5 | null | 2010-07-29T19:01:51.677 | 2010-07-29T19:01:51.677 | null | null | 36 | null |
1006 | 2 | null | 924 | 5 | null | In this case Poisson is good approximation for distribution for number of cases.
There is simple formula to approximate variance of log RR (delta method) .
log RR = 10/3 = 1.2,
se log RR = sqrt(1/3+1/10) = 0.66, so 95%CI = (-0.09; 2.5)
It is not significant difference at 0.05 level using two-sided test.
LR based Chi-s... | null | CC BY-SA 2.5 | null | 2010-07-29T19:22:17.873 | 2010-07-29T19:22:17.873 | null | null | 419 | null |
1008 | 2 | null | 871 | 2 | null | P value from theoretical point of view is some realization of random variable.
There is some standard (in probability) to use upper case letters for random variables and lower case for realizations.
In table headers we should use P (maybe italicize), in text together with its value p=0.0012 and in text describing for e... | null | CC BY-SA 2.5 | null | 2010-07-29T19:39:02.233 | 2010-07-29T19:39:02.233 | null | null | 419 | null |
1009 | 2 | null | 899 | 3 | null |
- For data in IQR range you should use
truncated normal distribution (for
example R package gamlss.tr) to
estimate parameters of this
distribution.
- Another approach is using mixture models with 2 or 3 components (distributions). You can fit such models using gamlss.mx package (distributions from package gamlss.... | null | CC BY-SA 2.5 | null | 2010-07-29T20:09:37.277 | 2010-07-29T20:09:37.277 | null | null | 419 | null |
1010 | 2 | null | 899 | 10 | null | If I understand correctly, then you can just fit a mixture of two Normals to the data. There are lots of R packages that are available to do this. This example uses the [mixtools](http://cran.r-project.org/web/packages/mixtools/index.html) package:
```
#Taken from the documentation
library(mixtools)
data(faithful)
atta... | null | CC BY-SA 2.5 | null | 2010-07-29T21:20:25.090 | 2010-07-29T21:20:25.090 | null | null | 8 | null |
1011 | 2 | null | 964 | 4 | null | Essentially they're looking for the log of the fold-change from one time point to the next because intuitively, it's easier to think about log-fold changes visually than actual fold-changes.
Log-fold changes make decreases and increases simply a difference in sign, so that a log-2-fold increase is the same distance as ... | null | CC BY-SA 2.5 | null | 2010-07-29T21:21:02.530 | 2010-07-29T21:21:02.530 | null | null | 378 | null |
1012 | 1 | 1014 | null | 2 | 2658 | A colleague wants to compare models that use either a Gaussian distribution or a uniform distribution and for other reasons needs the standard devation of these two distributions to be equal. In R I can do a simulation...
```
sd(runif(100000000))
sd(runif(100000000,min=0,max=2))
```
and see that the calculated standa... | What would the calculated value of the standard deviation of a uniform distribution be? | CC BY-SA 2.5 | null | 2010-07-29T21:29:57.590 | 2010-09-29T14:34:45.027 | 2010-08-12T07:19:30.733 | 196 | 196 | [
"distributions",
"uniform-distribution",
"normal-distribution"
] |
1013 | 2 | null | 1012 | 2 | null | The standard deviation of the continous uniform distribution on the interval [0,1] is 12-1/2≈0.288675. The [Wikipedia article](http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29) lists of it's more properties.
| null | CC BY-SA 2.5 | null | 2010-07-29T21:47:00.497 | 2010-09-29T14:34:45.027 | 2010-09-29T14:34:45.027 | 56 | 56 | null |
1014 | 2 | null | 1012 | 11 | null | In general, the standard deviation of a continous uniform distribution is (max - min) / sqrt(12).
| null | CC BY-SA 2.5 | null | 2010-07-29T21:54:24.130 | 2010-07-29T21:54:24.130 | null | null | 614 | null |
1015 | 1 | 3667 | null | 8 | 992 | I am trying to calculate the reliability in an elicitation exercise by analysing some test-retest questions given to the experts. The experts elicited a series of probability distributions which were then compared with the true value (found at a later date) by computing the standardized quadratic scores. These scores a... | Reliability in Elicitation Exercise | CC BY-SA 2.5 | null | 2010-07-29T22:03:55.887 | 2010-11-01T16:32:27.973 | 2010-10-18T15:21:53.397 | 930 | 108 | [
"psychometrics",
"reliability",
"elicitation"
] |
1016 | 1 | 1026 | null | 10 | 6877 | I've got a linear regression model with the sample and variable observations and I want to know:
- Whether a specific variable is significant enough to remain included in the model.
- Whether another variable (with observations) ought to be included in the model.
Which statistics can help me out? How can get them m... | Is a variable significant in a linear regression model? | CC BY-SA 2.5 | null | 2010-07-29T22:04:32.143 | 2010-09-16T23:06:05.980 | 2010-08-09T10:57:45.670 | 8 | 614 | [
"regression"
] |
1018 | 2 | null | 973 | 3 | null | [Arcene](http://archive.ics.uci.edu/ml/datasets/Arcene)
n=900
p=10000 (3k is artificially added noise)
k=2 (~balanced)
From [NIPS2003](http://www.nipsfsc.ecs.soton.ac.uk/papers/NIPS2003-Datasets.pdf).
| null | CC BY-SA 2.5 | null | 2010-07-29T22:30:13.867 | 2010-07-30T18:00:06.813 | 2010-07-30T18:00:06.813 | 190 | null | null |
1019 | 2 | null | 973 | 3 | null | [Dexter](http://archive.ics.uci.edu/ml/datasets/Dexter)
n=2600
p=20000 (10k+53 is artificial noise)
k=2 (balanced)
From [NIPS2003](http://www.nipsfsc.ecs.soton.ac.uk/papers/NIPS2003-Datasets.pdf).
| null | CC BY-SA 2.5 | null | 2010-07-29T22:32:44.880 | 2010-07-29T22:41:53.950 | 2010-07-29T22:41:53.950 | null | null | null |
1020 | 2 | null | 973 | 3 | null | [Dorothea](http://archive.ics.uci.edu/ml/datasets/Dorothea)
n=1950
p=100000 (0.1M, half is artificially added noise)
k=2 (~10x unbalanced)
From [NIPS2003](http://www.nipsfsc.ecs.soton.ac.uk/papers/NIPS2003-Datasets.pdf).
| null | CC BY-SA 2.5 | null | 2010-07-29T22:35:28.497 | 2010-07-29T22:41:33.450 | 2010-07-29T22:41:33.450 | null | null | null |
1021 | 2 | null | 973 | 3 | null | [Gisette](http://archive.ics.uci.edu/ml/datasets/Gisette)
n=13500
p=5000 (half is artificially added noise)
k=2 (balanced)
From [NIPS2003](http://www.nipsfsc.ecs.soton.ac.uk/papers/NIPS2003-Datasets.pdf).
| null | CC BY-SA 2.5 | null | 2010-07-29T22:38:21.253 | 2010-07-29T22:38:21.253 | null | null | null | null |
1023 | 1 | 1029 | null | 12 | 1417 | Introductory, advanced, and even obscure, please.
Mostly to test myself. I like to make sure I know what the heck I'm talking about :)
Thanks
| Where can I find good statistics quizzes? | CC BY-SA 2.5 | null | 2010-07-29T23:04:08.707 | 2010-08-17T20:09:56.013 | null | null | 74 | [
"teaching"
] |
1024 | 2 | null | 1016 | 3 | null | For part 1, you're looking for the [F-test](http://en.wikipedia.org/wiki/F-test#Regression_problems). Calculate your residual sum of squares from each model fit and calculate an F-statistic, which you can use to find p-values from either an F-distribution or some other null distribution that you generate yourself.
| null | CC BY-SA 2.5 | null | 2010-07-29T23:25:12.603 | 2010-07-29T23:25:12.603 | null | null | 378 | null |
1025 | 2 | null | 980 | 7 | null | Calculating averages leads to a different dataset than simply reducing the number of data points.
If one heartbeat per minute is much faster than the other heart beats you will lose the signal through your smoothing process.
If you summary 125-125-0-125-125 as 100 than the story that the data tells is different through... | null | CC BY-SA 2.5 | null | 2010-07-29T23:45:46.407 | 2010-07-31T02:09:30.503 | 2010-07-31T02:09:30.503 | 3807 | 3807 | null |
1026 | 2 | null | 1016 | 27 | null | Statistical significance is not usually a good basis for determining whether a variable should be included in a model. Statistical tests were designed to test hypotheses, not select variables. I know a lot of textbooks discuss variable selection using statistical tests, but this is generally a bad approach. See Harrell... | null | CC BY-SA 2.5 | null | 2010-07-30T00:00:15.037 | 2010-07-30T00:00:15.037 | null | null | 159 | null |
1027 | 2 | null | 1015 | 0 | null | You would use Cronbach alpha if you do not know the true value but if you do know the true value then it seems a bit pointless to use Cronbach alpha. The use of Pearson correlation also seems a bit odd as you do not actually have a paired set of values. I would suggest using something like the [Mean Squared Error (MSE)... | null | CC BY-SA 2.5 | null | 2010-07-30T00:44:09.457 | 2010-07-30T00:44:09.457 | null | null | null | null |
1028 | 1 | null | null | 14 | 9844 | I am comparing two distributions with KL divergence which returns me a non-standardized number that, according to what I read about this measure, is the amount of information that is required to transform one hypothesis into the other. I have two questions:
a) Is there a way to quantify a KL divergence so that it has a... | How to interpret KL divergence quantitatively? | CC BY-SA 4.0 | null | 2010-07-30T00:55:20.603 | 2022-04-29T14:52:25.830 | 2022-04-29T14:52:25.830 | 60613 | 608 | [
"distributions",
"kullback-leibler",
"information-geometry"
] |
1029 | 2 | null | 1023 | 7 | null | I wrote a post compiling links of Practice Questions for Statistics in Psychology (Undergraduate Level).
[http://jeromyanglim.blogspot.com/2009/12/practice-questions-for-statistics-in.html](http://jeromyanglim.blogspot.com/2009/12/practice-questions-for-statistics-in.html)
The questions would fall into the introductor... | null | CC BY-SA 2.5 | null | 2010-07-30T02:42:38.523 | 2010-07-30T02:42:38.523 | null | null | 183 | null |
1030 | 2 | null | 1028 | 7 | null | The KL(p,q) divergence between distributions p(.) and q(.) has an intuitive information theoretic interpretation which you may find useful.
Suppose we observe data x generated by some probability distribution p(.). A lower bound on the average codelength in bits required to state the data generated by p(.) is given by... | null | CC BY-SA 2.5 | null | 2010-07-30T03:53:08.320 | 2010-07-30T03:53:08.320 | null | null | 530 | null |
1031 | 2 | null | 1028 | 9 | null | KL has a deep meaning when you visualize a set of dentities as a manifold within the fisher metric tensor, it gives the geodesic distance between two "close" distributions. Formally:
$ds^2=2KL(p(x, \theta ),p(x,\theta + d \theta))$
The following lines are here to explain with details what is meant by this las mathemati... | null | CC BY-SA 4.0 | null | 2010-07-30T05:29:11.857 | 2022-04-29T14:38:29.267 | 2022-04-29T14:38:29.267 | -1 | 223 | null |
1032 | 2 | null | 414 | 7 | null | You will find many applications of Mathematical Statistics in '[Mathematical Statistics and Data Analysis](https://rads.stackoverflow.com/amzn/click/com/0534399428)' by John A. Rice. The 'Application Index' lists all applications discussed in the text.
| null | CC BY-SA 4.0 | null | 2010-07-30T08:04:50.453 | 2023-02-11T10:06:28.840 | 2023-02-11T10:06:28.840 | 362671 | 531 | null |
1033 | 2 | null | 887 | 0 | null | "how is stdev(S) related to the standard deviation of the entire population?"
I don't know if the "Confidence Interval" concept might be what you are looking for?
Stdev(S) is an Estimate of the standard deviation of the entire population. To see how good an estimate, confidence intervals could be computed, and these w... | null | CC BY-SA 2.5 | null | 2010-07-30T09:49:02.353 | 2010-07-30T09:49:02.353 | null | null | null | null |
1035 | 2 | null | 913 | 4 | null | Another solution to your problem (without transforming variables) is regression with error distribution other then Gaussian for example Gamma or skewed t-Student.
Gamma is in GLM family, so there is a lot of software to fit model with this error distribution.
| null | CC BY-SA 2.5 | null | 2010-07-30T09:54:10.323 | 2010-07-30T09:54:10.323 | null | null | 419 | null |
1036 | 2 | null | 125 | 32 | null | Sivia and Skilling, Data analysis: a Bayesian tutorial (2ed) 2006 246p 0198568320
[books.goo](http://books.google.com/books?id=zN-yliq6eZ4C&dq=isbn:0198568320&source=gbs_navlinks_s):
>
Statistics lectures have been a source
of much bewilderment and frustration
for generations of students. This book
attempts to r... | null | CC BY-SA 2.5 | null | 2010-07-30T10:03:37.523 | 2010-07-30T10:03:37.523 | null | null | 557 | null |
1038 | 2 | null | 652 | 1 | null |
- Wilcox, Rand R. - BASIC STATISTICS - Understanding
Conventional Methods and Modern
Insights, Oxford University Press,
2009
- Hoff, Peter D. - A First Course in
Bayesian Statistical Methods,
Springer, 2009
- Dalgaard, Peter - Introductory
Statistics with R, Second Edition, Springer, 2008
also take a glance at [th... | null | CC BY-SA 2.5 | null | 2010-07-30T12:11:18.743 | 2010-07-30T12:11:18.743 | 2017-05-23T12:39:26.150 | -1 | 1356 | null |
1039 | 2 | null | 125 | 10 | null | If you're looking for an elementary text, i.e. one that doesn't have a calculus prerequisite, there's Don Berry's [Statistics: A Bayesian Perspective](http://rads.stackoverflow.com/amzn/click/0534234720).
| null | CC BY-SA 2.5 | null | 2010-07-30T12:15:45.737 | 2010-07-30T12:15:45.737 | null | null | 319 | null |
1040 | 1 | 1041 | null | 4 | 1107 | Consider the following model
$Y_i = f(X_i) + e_i$
from which we observe n iid data points $\left( X_i, Y_i \right)_{i=1}^n$. Suppose that $X_i \in \mathbb{R}^d$ is a $d$ dimensional feature vector. And suppose that a ordinary least squares estimate is fit to data, that is,
$\hat \beta = {\rm arg} \min_{\beta \in \math... | What is the interpretation/meaning of confidence intervals in misspecified models? | CC BY-SA 2.5 | null | 2010-07-30T14:24:17.350 | 2017-04-23T18:02:00.423 | 2017-04-23T18:02:00.423 | 11887 | 168 | [
"confidence-interval",
"estimation",
"modeling",
"model-selection",
"misspecification"
] |
1041 | 2 | null | 1040 | 3 | null | The confidence interval that you obtain is conditional on the model being correct and the interpretation is also conditional on the model being the correct one. If you know that the model is incorrect then obviously you would not use it to compute the confidence interval.
In reality, you do not know the true model and... | null | CC BY-SA 2.5 | null | 2010-07-30T14:38:39.530 | 2010-07-30T14:38:39.530 | null | null | null | null |
1043 | 2 | null | 977 | 1 | null | OK, so your data is very expensive to get. If you have some indication of the shape of the data then perhaps a bootstrap / bayesian / optimization (sticking in keywords :)) approach would work best. See the optim command in R as an example. You would need to know some things though. For example, could we assume som... | null | CC BY-SA 2.5 | null | 2010-07-30T15:37:06.190 | 2010-07-30T15:37:06.190 | null | null | 601 | null |
1044 | 2 | null | 73 | 6 | null | Sweave lets you embed R code in a LaTeX document. The results of executing the code, and optionally the source code, become part of the final document.
So instead of, for example, pasting an image produced by R into a LaTeX file, you can paste the R code into the file and keep everything in one place.
| null | CC BY-SA 2.5 | null | 2010-07-30T15:43:28.517 | 2010-07-30T15:43:28.517 | null | null | 319 | null |
1045 | 1 | 1051 | null | 4 | 1215 | The wiki article on [credible intervals](http://en.wikipedia.org/wiki/Credible_interval) has the following statement:
>
credible intervals and confidence intervals treat nuisance parameters in radically different ways.
What is the radical difference that the wiki talks about?
Credible intervals are based on the post... | Is there a radical difference in how bayesian and frequentist approaches treat nuisance parameters? | CC BY-SA 2.5 | null | 2010-07-30T16:06:54.980 | 2010-07-30T23:53:35.657 | 2017-04-13T12:44:54.643 | -1 | null | [
"confidence-interval",
"credible-interval"
] |
1046 | 2 | null | 825 | 6 | null | If you are using GNU/Linux previous answers by Shane and Dirk are great.
If you need a solution for windows, there is one in this post:
[Parallel Multicore Processing with R (on Windows)](http://www.r-statistics.com/2010/04/parallel-multicore-processing-with-r-on-windows/)
Although the package is not yet on CRAN. it ca... | null | CC BY-SA 2.5 | null | 2010-07-30T16:58:42.397 | 2010-07-30T16:58:42.397 | null | null | 253 | null |
1047 | 1 | 1048 | null | 42 | 35196 | I'm comparing a sample and checking whether it distributes as some, discrete, distribution. However, I'm not enterily sure that Kolmogorov-Smirnov applies. [Wikipedia](http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) seems to imply it does not. If it does not, how can I test the sample's distribution?
| Is Kolmogorov-Smirnov test valid with discrete distributions? | CC BY-SA 2.5 | null | 2010-07-30T17:00:57.573 | 2018-08-28T00:33:33.743 | 2018-08-28T00:33:33.743 | 805 | 614 | [
"hypothesis-testing",
"discrete-data",
"kolmogorov-smirnov-test"
] |
1048 | 2 | null | 1047 | 19 | null | It does not apply to discrete distributions. See [http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm](http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm) for example.
Is there any reason you can't use a chi-square goodness of fit test?
see [http://www.itl.nist.gov/div898/handbook/eda/section3/... | null | CC BY-SA 2.5 | null | 2010-07-30T17:10:09.663 | 2010-07-30T17:10:09.663 | null | null | 247 | null |
1049 | 2 | null | 138 | 8 | null | One resource is 'Some hints for the R beginner' at
[http://www.burns-stat.com/pages/Tutor/hints_R_begin.html](http://www.burns-stat.com/pages/Tutor/hints_R_begin.html)
| null | CC BY-SA 2.5 | null | 2010-07-30T17:10:24.607 | 2010-07-30T17:10:24.607 | null | null | null | null |
1050 | 2 | null | 170 | 16 | null | Here's a fresh one: [Introduction to Probability and Statistics Using R ](https://rdrr.io/cran/IPSUR/f/inst/doc/IPSUR.pdf). It's R-specific, though, but it's a great one. I haven't read it yet, but it seems fine so far...
| null | CC BY-SA 4.0 | null | 2010-07-30T20:00:45.253 | 2021-05-31T03:50:57.847 | 2021-05-31T03:50:57.847 | 287839 | 1356 | null |
1051 | 2 | null | 1045 | 5 | null | The fundamental difference is that in maximum likelihood based methods we can't integrate the nuisance parameters out (because the likelihood function is not a PDF and doesn't obey probability laws).
In maximum likelihood methods, the ideal way to deal with nuisance parameters is through marginal/conditional likelihood... | null | CC BY-SA 2.5 | null | 2010-07-30T21:15:43.290 | 2010-07-30T23:53:35.657 | 2010-07-30T23:53:35.657 | 251 | 251 | null |
1052 | 1 | null | null | 15 | 12681 | my question particularly applies to network reconstruction
| What is the major difference between correlation and mutual information? | CC BY-SA 2.5 | null | 2010-07-30T22:38:06.510 | 2010-07-31T03:04:03.400 | 2010-07-31T02:05:21.060 | null | null | [
"correlation",
"mutual-information"
] |
1053 | 1 | 1056 | null | 45 | 10662 | I am looking for a good book/tutorial to learn about survival analysis. I am also interested in references on doing survival analysis in R.
| References for survival analysis | CC BY-SA 3.0 | null | 2010-07-31T00:51:52.653 | 2022-05-12T10:46:29.413 | 2015-11-04T18:17:45.123 | 22468 | 172 | [
"r",
"survival",
"references"
] |
1054 | 1 | 1058 | null | 5 | 1763 | What is the equivalent command in R for the `stcox` command in Stata?
| R command for stcox in Stata | CC BY-SA 3.0 | null | 2010-07-31T00:59:11.343 | 2013-07-20T22:57:03.343 | 2013-07-20T22:57:03.343 | 22047 | 172 | [
"r",
"survival",
"stata"
] |
1055 | 2 | null | 1052 | 25 | null | Correlation measures the linear relationship (Pearson's correlation) or monotonic relationship (Spearman's correlation) between two variables, X and Y.
Mutual information is more general and measures the reduction of uncertainty in Y after observing X. It is the KL distance between the joint density and the product of... | null | CC BY-SA 2.5 | null | 2010-07-31T01:08:08.763 | 2010-07-31T03:04:03.400 | 2010-07-31T03:04:03.400 | 159 | 159 | null |
1056 | 2 | null | 1053 | 21 | null | I like:
- Survival Analysis: Techniques for Censored and Truncated Data (Klein & Moeschberger)
- Modeling Survival Data: Extending the Cox Model (Therneau)
The first does a good job of straddling theory and model building issues. It's mostly focused on semi-parametric techniques, but there is reasonable coverage o... | null | CC BY-SA 3.0 | null | 2010-07-31T01:41:53.177 | 2017-01-18T22:35:34.987 | 2017-01-18T22:35:34.987 | 251 | 251 | null |
1057 | 2 | null | 1052 | 4 | null | To add to Rob's answer ... with respect to reverse engineering a network, MI may be preferred over correlation when you want to extract causal rather than associative links in your network. Correlation networks are purely associative. But for MI, you need more data and computing power.
| null | CC BY-SA 2.5 | null | 2010-07-31T02:05:44.990 | 2010-07-31T02:05:44.990 | null | null | 251 | null |
1058 | 2 | null | 1054 | 9 | null | In package [survival](http://cran.r-project.org/web/packages/survival/index.html), it's `coxph`. John Fox has a nice introduction to using coxph in R:
- Cox Proportional-Hazards Regression for Survival Data
| null | CC BY-SA 2.5 | null | 2010-07-31T02:08:06.560 | 2010-09-19T07:23:51.393 | 2010-09-19T07:23:51.393 | 930 | 251 | null |
1059 | 2 | null | 726 | 37 | null | >
"Million to one chances crop up nine times out of ten."
-[Terry Pratchett](http://www.goodreads.com/quotes/95458-scientists-have-calculated-that-the-chances-of-something-so-patently)
| null | CC BY-SA 3.0 | null | 2010-07-31T05:49:03.187 | 2012-11-03T06:15:39.000 | 2012-11-03T06:15:39.000 | 9007 | 183 | null |
1060 | 1 | 1061 | null | 7 | 1347 | I'll use an example so that you can reproduce the results
```
# mortality
mort = ts(scan("http://www.stat.pitt.edu/stoffer/tsa2/data/cmort.dat"),start=1970, frequency=52)
# temperature
temp = ts(scan("http://www.stat.pitt.edu/stoffer/tsa2/data/temp.dat"), start=1970, frequency=52)
#pollutant particulates
part = ts(... | Why does AIC formula in R appear to use one extra parameter than expected? | CC BY-SA 3.0 | null | 2010-07-31T09:39:47.323 | 2011-04-29T05:03:59.523 | 2011-04-29T05:03:59.523 | 183 | 339 | [
"r",
"time-series",
"modeling",
"aic"
] |
1061 | 2 | null | 1060 | 11 | null | ```
> -2*logLik(fit)+2*(length(fit$coef)+1)
[1] 3332.282
```
(you forgot; you have 6 parameter because $\sigma_{\epsilon}$ also has to be estimated!
| null | CC BY-SA 3.0 | null | 2010-07-31T10:04:04.443 | 2011-04-29T01:10:38.227 | 2011-04-29T01:10:38.227 | 3911 | 603 | null |
1062 | 1 | null | null | 6 | 3747 | In general inference, why orthogonal parameters are useful, and why is it worth trying to find a new parametrization that makes the parameters orthogonal ?
I have seen some textbook examples, not so many, and would be interested in more concrete examples and/or motivation.
| Orthogonal parametrization | CC BY-SA 2.5 | null | 2010-07-31T14:19:13.850 | 2020-09-27T06:27:40.240 | 2020-09-27T06:27:40.240 | 7290 | 368 | [
"multivariate-analysis",
"information-geometry"
] |
1063 | 1 | 1065 | null | 19 | 92828 | My stats has been self taught, but a lot of material I read point to a dataset having mean 0 and standard deviation of 1.
If that is the case then:
- Why is mean 0 and SD 1 a nice property to have?
- Why does a random variable drawn from this sample equal 0.5? The chance of drawing 0.001 is the same as 0.5 so this s... | Why are mean 0 and standard deviation 1 distributions always used? | CC BY-SA 3.0 | null | 2010-07-31T14:29:15.543 | 2022-03-30T16:27:40.373 | 2012-08-20T04:54:05.637 | 2116 | 353 | [
"probability"
] |
1064 | 2 | null | 726 | 80 | null | A nice one I came about:
>
I think it's much more interesting to live not knowing than to have answers which might be wrong.
By Richard Feynman ([link](https://www.youtube.com/watch?v=I1tKEvN3DF0))
| null | CC BY-SA 3.0 | null | 2010-07-31T15:36:12.410 | 2016-07-10T03:35:26.313 | 2016-07-10T03:35:26.313 | null | 253 | null |
1065 | 2 | null | 1063 | 11 | null |
- At the beginning the most useful answer is probably that mean of 0 and sd of 1 are mathematically convenient. If you can work out the probabilities for a distribution with a mean of 0 and standard deviation of 1 you can work them out for any similar distribution of scores with a very simple equation.
- I'm not fol... | null | CC BY-SA 3.0 | null | 2010-07-31T15:46:05.483 | 2012-08-20T04:56:16.753 | 2012-08-20T04:56:16.753 | 2116 | 601 | null |
1066 | 1 | 1068 | null | 2 | 497 | My question is actually quite short, but I'll have to start by describing the context since I am not sure how to directly ask it.
Consider the following "game":
We have a segment of length n ("large segment") and m integers ("lengths"), all considerably smaller than n. For each of the m lengths we draw a random sub-se... | Approximating density function for a non-normal distribution | CC BY-SA 2.5 | null | 2010-07-31T18:20:26.487 | 2012-05-18T12:35:06.850 | 2010-09-30T21:24:14.807 | 930 | 634 | [
"distributions",
"normality-assumption"
] |
1068 | 2 | null | 1066 | 4 | null | I'm not sure I understand your question exactly, but I assume you are looking for the probability mass at each point, where an event is defined as a subsegment covering a particular position. If this is true, I believe you should be able to work out the exact probability mass function.
For each subsegment of length K ... | null | CC BY-SA 2.5 | null | 2010-07-31T18:50:56.847 | 2010-07-31T19:06:43.163 | 2010-07-31T19:06:43.163 | 493 | 493 | null |
1069 | 2 | null | 1062 | 7 | null | This is a good, if underspecified question.
Simply put, obtaining an [orthogonal](http://en.wikipedia.org/wiki/Orthogonal_coordinates) parametrization allows for parameters of interest to be conveniently related to other parameters, particularly in establishing needed minimizations. Whether or not this is useful depe... | null | CC BY-SA 2.5 | null | 2010-07-31T19:47:20.103 | 2010-10-12T16:05:37.587 | 2010-10-12T16:05:37.587 | 39 | 39 | null |
1070 | 2 | null | 1062 | 9 | null | In Maximum Likelihood, the term orthogonal parameters is used when you can achieve a clean factorization of a multi-parameter likelihood function. Say your data have two parameters $\theta$ and $\lambda$. If you can rewrite the joint likelihood:
$L(\theta, \lambda) = L_{1}(\theta) L_{2}(\lambda)$
then we call $\theta... | null | CC BY-SA 2.5 | null | 2010-07-31T20:17:03.217 | 2010-08-01T04:31:00.753 | 2017-04-13T12:44:46.433 | -1 | 251 | null |
1072 | 2 | null | 1016 | 4 | null | I second Rob's comment. An increasingly prefered alternative is to include all your variables and shrink them towards 0. See Tibshirani, R. (1996). Regression shrinkage and selection via the lasso.
[http://www-stat.stanford.edu/~tibs/lasso/lasso.pdf](http://www-stat.stanford.edu/~tibs/lasso/lasso.pdf)
| null | CC BY-SA 2.5 | null | 2010-07-31T21:22:55.277 | 2010-07-31T21:22:55.277 | null | null | 603 | null |
1073 | 2 | null | 373 | 25 | null | To answer the original question: Our intuition fails because of the narrative. By relating the story in the same order as the tv script, we get confused. It gets much easier if we think about what is going to happen in advance. The quiz-master will reveal a goat, so our best chance is to select a door with a goat an... | null | CC BY-SA 3.0 | null | 2010-07-31T21:33:59.133 | 2015-08-04T11:14:48.160 | 2015-08-04T11:14:48.160 | 638 | 638 | null |
1074 | 2 | null | 373 | 2 | null | The lesson? Reformulate the question, and search for a strategy instead of looking at the situation. Turn the thing on its head, work backwards...
People are generally bad at working with chance. Animals typically fare better, once they discover that either A or B gives a higher payout on average; they stick to the ... | null | CC BY-SA 2.5 | null | 2010-07-31T22:01:41.933 | 2010-07-31T22:01:41.933 | null | null | 638 | null |
1079 | 2 | null | 146 | 7 | null | Careful... just because the PCs are by construction orthogonal to each other does not mean that there is not a pattern or that one PC can not appear to "explain" something about the other PCs.
Consider 3D data (X,Y,Z) describing a large number of points distributed evenly on the surface of an American football (it is a... | null | CC BY-SA 2.5 | null | 2010-08-01T06:26:34.787 | 2010-08-01T23:22:03.657 | 2010-08-01T23:22:03.657 | 87 | 87 | null |
1080 | 2 | null | 373 | 2 | null | I think there are several things going on.
For one, the setup implies more information then the solution takes into account. That it is a game show, and the host is asking us if we want to switch.
If you assume the host does not want the show to spend extra money (which is reasonable), then you would assume he would tr... | null | CC BY-SA 2.5 | null | 2010-08-01T06:53:28.040 | 2010-08-01T06:53:28.040 | null | null | 572 | null |
1081 | 1 | 1106 | null | 5 | 1202 | I have been reading Zuur, Ieno and Smith (2007) Analyzing ecological data, and on page 262, they try to explain how nMDS (non-metric multidimensional scaling) algorithm works. As my background is in biology and not math or statistics per se, I'm having hard time understanding a few points and would ask you if you could... | Help me understand nMDS algorithm | CC BY-SA 2.5 | null | 2010-08-01T07:16:03.853 | 2010-09-30T21:23:34.420 | 2010-09-30T21:23:34.420 | 930 | 144 | [
"nonparametric",
"multidimensional-scaling"
] |
1082 | 1 | null | null | 5 | 1877 | I am trying to assess the significance of the obtained MI matrix. The initial input was a array of 3000 genes by 45 timepoints. MI was computed resulting in a array of 3600 by 3600. I am thus comparing my results to a shuffled matrix with the same dimensions. I permutate the columns 100 times, thus have 100 results fo... | How to define the significance threshold for mutual information in terms of probability of that value occurring in surrogate set? | CC BY-SA 2.5 | null | 2010-08-01T11:48:48.670 | 2010-08-01T18:41:27.523 | null | null | null | [
"correlation"
] |
1083 | 2 | null | 95 | 3 | null | Generally, by not allowing for assymetry, you expect the effect of shocks to last longers: i.e. the half-life increases (the half life is the number of units of time, after a 1 S.D. shock to $\epsilon_{t-1}$ for $\hat{\sigma}t|I{t-1}$ to come back to the its unconditional value.)
Here is a code snipped that downloads ... | null | CC BY-SA 2.5 | null | 2010-08-01T11:56:51.260 | 2010-08-01T13:38:50.213 | 2010-08-01T13:38:50.213 | 603 | 603 | null |
1084 | 1 | 1086 | null | 4 | 1806 | I'm trying to visualize a set of data that represents human body mass over time, taken from (usually) daily weighings.
Because body mass tends to fluctuate +/- 3 pounds based on hydration I would like to draw a strongly smoothed line graph to minimize the fluctuation.
Any help on what the equation would look like is mu... | Equation to calculate a smooth line given an irregular time series? | CC BY-SA 2.5 | null | 2010-08-01T12:08:02.313 | 2010-08-13T11:29:34.633 | 2010-08-13T11:29:34.633 | 159 | 642 | [
"data-visualization",
"smoothing"
] |
1086 | 2 | null | 1084 | 6 | null | There are several methods of estimating a smooth trend line. Some of the most popular are:
- Moving average smoother
- Local linear regression (loess being a popular robust implementation)
- Smoothing splines
- Regression splines (of various flavors).
My preference is to use penalized regression splines which yo... | null | CC BY-SA 2.5 | null | 2010-08-01T12:29:28.977 | 2010-08-01T12:48:13.933 | 2010-08-01T12:48:13.933 | 159 | 159 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.