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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4596 | 2 | null | 4580 | 6 | null | I am a biologist who models the effects of inter-annual climatic variation on population dynamics of several migratory species. My datasets are very large (spatially intensive data) so I run my R code using `multicore` on Amazon EC2 servers. If my task is particularly resource intensive, I will choose a High Memory Qua... | null | CC BY-SA 2.5 | null | 2010-11-16T19:10:24.867 | 2010-11-16T19:10:24.867 | null | null | 1451 | null |
4597 | 1 | null | null | 4 | 1111 | I am trying to detect text in a scanned document by examining variations in the lightness of the scan collapsed vertically. Here's a sample of the input I would receive, with the lightness plot of each vertical pixel strip superimposed:
Note: I've applied a Gaussian smoot... | How to find text blocks in a scanned document? | CC BY-SA 3.0 | null | 2010-11-16T20:41:34.210 | 2012-05-03T23:14:21.130 | 2012-05-03T23:14:21.130 | 4479 | 2002 | [
"python",
"image-processing"
] |
4598 | 2 | null | 4597 | 1 | null | What about Moving Averages?
Edit: For calculating moving standard deviations, this is a quick and dirty way to do it in R:
```
n.x <- 1000
x <- cumsum(rnorm(n.x))
plot(x,type="l")
win <- 20
roll.sd <- as.vector(rep(NA,n.x))
for(i in 1:(n.x-win)){roll.sd[i] <- sd(x[i:(i+win)])}
```
I think quantmod has a build-in funct... | null | CC BY-SA 2.5 | null | 2010-11-16T21:05:45.083 | 2010-11-17T12:15:25.700 | 2010-11-17T12:15:25.700 | 1766 | 1766 | null |
4599 | 2 | null | 3194 | 9 | null | Nobody has suggested a Bayesian approach yet? I know the question has been answered already, but what the heck. Below is for only a 3-sided die, but I'm guessing it's obvious how to fix it for $n=37$ sides.
First, in line with what @Glen_b said, a bayesian is not actually interested whether or not the die is exactly f... | null | CC BY-SA 2.5 | null | 2010-11-16T21:24:33.410 | 2010-11-16T21:24:33.410 | null | null | null | null |
4600 | 1 | null | null | 3 | 3067 | I have run a factorial type test in a processing plant and have run a forward and backward step regression in R.
How can I use the regression results and the anova created from the regression to know what percent of the measured variation of the dependent variable was caused by the purposeful manipulation of the indep... | Explaining variation in a dependent variable based on a factorial experiment | CC BY-SA 3.0 | null | 2010-11-16T22:11:41.093 | 2011-06-06T04:50:30.013 | 2011-06-06T04:50:30.013 | 183 | null | [
"regression",
"anova",
"interpretation"
] |
4602 | 2 | null | 4595 | 3 | null | The [travelling salesman problem](http://en.wikipedia.org/wiki/Travelling_salesman_problem) is surely an archetypal hard optimization problem. To quote Wikipedia, "it is used as a benchmark for many optimization methods".
| null | CC BY-SA 2.5 | null | 2010-11-16T22:29:48.383 | 2010-11-16T22:29:48.383 | null | null | 449 | null |
4603 | 1 | 4614 | null | 5 | 3323 | Are there any open-source Java implementations of lasso or least angles regression?
Pure Java code would be best, but clean implementations in other languages would also be of interest. I am already aware of the existence of a variety of R packages than can do lasso/LAR fits.
Thanks.
| Java implementations of the lasso | CC BY-SA 2.5 | null | 2010-11-16T22:31:23.040 | 2011-06-07T00:09:52.463 | 2010-11-16T22:53:34.213 | 439 | 439 | [
"regression",
"lasso",
"java"
] |
4604 | 1 | 4613 | null | 5 | 416 | I'm doing some some analysis of an arbitrary string of text, modelling it as a Markov chain where the state is simply the value of the previous character. Call the current character $c$ and the previous character $p$; then it is trivial to calculate $P(c\ |\ p)$ for the given sample text. However, if there are not many... | Uncertainty of conditional probability evaluated from sample | CC BY-SA 4.0 | null | 2010-11-16T22:57:41.137 | 2021-01-14T17:12:00.303 | 2021-01-14T17:12:00.303 | 1810 | 1810 | [
"markov-process",
"conditional-probability"
] |
4605 | 2 | null | 4580 | 2 | null | I use snow and snowfall for course parallelization on HPC clusters and CUDA for fine data parallel processing. I'm in Epidemiology doing disease transmission modeling. So I use both.
| null | CC BY-SA 2.5 | null | 2010-11-17T01:34:10.317 | 2010-11-17T01:34:10.317 | null | null | 1364 | null |
4606 | 1 | null | null | 3 | 535 | I am thinking of using [this code](http://www.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-estimator) in a Monte Carlo routine to generate Kernel Density Estimates for subsequent use in a Naive Bayes Classifier [(see this earlier post)](https://stats.stackexchange.com/questions/4298/use-of-kernel-densi... | Averaged continuous Kernel Density Estimates in lieu of a discrete Kernel Density Estimate in Monte Carlo Proceedure | CC BY-SA 2.5 | null | 2010-11-17T02:04:27.900 | 2010-11-18T16:35:12.947 | 2017-04-13T12:44:44.530 | -1 | 226 | [
"kde",
"discrete-data",
"continuous-data",
"monte-carlo"
] |
4607 | 2 | null | 2171 | 1 | null | Another good alternative is the protovis library [http://vis.stanford.edu/protovis/](http://vis.stanford.edu/protovis/"Protovis")
It is a very well crafted JavaScript library that can create some beautiful visualizations if you have the time and ability to write the modest amount of JavaScript code needed.
I also highl... | null | CC BY-SA 3.0 | null | 2010-11-17T02:44:28.000 | 2014-11-15T13:45:50.507 | 2014-11-15T13:45:50.507 | 22047 | 1246 | null |
4608 | 1 | 4621 | null | 37 | 67009 | I'm trying to implement basic gradient descent and I'm testing it with a hinge loss function i.e. $l_{\text{hinge}} = \max(0,1-y\ \boldsymbol{x}\cdot\boldsymbol{w})$. However, I'm confused about the gradient of the hinge loss. I'm under the impression that it is
$$
\frac{\partial }{\partial w}l_{\text{hinge}} =
\begin... | Gradient of Hinge loss | CC BY-SA 3.0 | null | 2010-11-17T03:15:58.023 | 2017-05-29T14:31:53.233 | 2014-05-03T15:02:22.587 | 27403 | 2023 | [
"loss-functions"
] |
4609 | 1 | 9919 | null | 1 | 1285 | from what I can tell, PASW v.18 (the new version of SPSS) only gives you the p value of nonparametric tests. I am calculating Kruskal Wallis and Mann Whitney tests, and need to report the test statistic, not just p. Can someone please help?
Thanks!
| How do I get non-parametric test values (not just p) in PASW 18? | CC BY-SA 2.5 | null | 2010-11-17T03:19:10.763 | 2011-04-24T04:00:58.850 | null | null | 2025 | [
"nonparametric",
"spss"
] |
4610 | 1 | null | null | 3 | 1696 |
Statistics were never my strong point and it's my first question, so please be gentle :) I'm doing some research using Computational Fluid Dynamics, CFD, to model the flow of an oil aerosol through a fibrous filter. The aerosol has a droplet size distribution that is log-normal. The existing code only allows the s... | Log normal distributions - particle sizes in an aerosol | CC BY-SA 2.5 | null | 2010-11-17T03:33:13.613 | 2010-11-18T01:22:50.987 | 2010-11-18T01:22:50.987 | null | null | [
"distributions",
"lognormal-distribution"
] |
4611 | 2 | null | 4368 | 2 | null | Note that
$\text{Var}(\epsilon | x_i) = E[\epsilon^2 | x_i] - \left(E[\epsilon | x_i]\right)^2.$
Recall a typical Gauss-Markov assumption that $E[\epsilon | x_i]=0$. Hence,
$\text{Var}(\epsilon | x_i) = E[\epsilon^2 | x_i].$
Since we don't observe $\epsilon^2$, we have to use some estimate. The best estimate that we ... | null | CC BY-SA 2.5 | null | 2010-11-17T04:00:28.513 | 2010-11-17T04:00:28.513 | null | null | 401 | null |
4612 | 1 | 4615 | null | 26 | 34462 | Which good econometrics textbooks would you recommend?
Edit: there are quite a few books out there, with varying levels of mathematical sophistication. It would be good to get some idea of how technical the book you're recommending is.
| Econometrics textbooks? | CC BY-SA 3.0 | null | 2010-11-17T07:28:49.070 | 2021-02-14T19:01:17.233 | 2015-11-02T01:19:35.897 | 22468 | 439 | [
"econometrics",
"references"
] |
4613 | 2 | null | 4604 | 4 | null | You can use the [Hoeffding inequality](http://en.wikipedia.org/wiki/Hoeffding%27s_inequality):
$$ P(|\hat{p}_n-p|\geq t)\leq 2e^{-2nt^2}$$
($\hat{p}_n$ is your estimated probability).
For small $n$, Markov ineqality may be more efficient:
$$P(|\hat{p}_n-p|\geq t)\leq \frac{p(1-p)}{nt^2} $$.
For example, with the se... | null | CC BY-SA 2.5 | null | 2010-11-17T07:30:42.387 | 2010-11-23T14:37:19.163 | 2010-11-23T14:37:19.163 | 223 | 223 | null |
4614 | 2 | null | 4603 | 5 | null | About clean implementation in Python, there is the [scikit.learn](http://scikit-learn.sourceforge.net/) toolkit. The [L1/L2 regularization scheme](http://scikit-learn.sourceforge.net/modules/glm.html) (incl. elasticnet) works great with GLM (LARS and coordinate descent algorithms available). Don't know about Java imple... | null | CC BY-SA 2.5 | null | 2010-11-17T07:34:31.840 | 2010-11-17T07:34:31.840 | null | null | 930 | null |
4615 | 2 | null | 4612 | 14 | null | Definitively [Econometric Analysis](http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm), by Greene. I'm not an econometrician, but I found this book very useful and well written.
| null | CC BY-SA 2.5 | null | 2010-11-17T07:40:10.787 | 2010-11-17T07:40:10.787 | null | null | 930 | null |
4616 | 2 | null | 4610 | 3 | null | Saying 'the average is 425nm' is probably not the best way to frame the input. You're better parameterising a [log-normal distribution](http://en.wikipedia.org/wiki/Log-normal_distribution) by its [geometric mean](http://en.wikipedia.org/wiki/Geometric_mean) and [geometric standard deviation](http://en.wikipedia.org/wi... | null | CC BY-SA 2.5 | null | 2010-11-17T07:44:20.333 | 2010-11-17T07:44:20.333 | null | null | 449 | null |
4617 | 2 | null | 4612 | 9 | null | Depends on what level you're after. At a postgraduate level, the one i've most often seen referenced and recommended, and have therefore looked at most myself, is:
Wooldridge, Jeffrey M. Econometric Analysis of Cross Section and Panel Data. MIT Press, 2001. ISBN [9780262232197](http://en.wikipedia.org/w/index.php?title... | null | CC BY-SA 2.5 | null | 2010-11-17T07:56:14.970 | 2010-11-17T07:56:14.970 | null | null | 449 | null |
4618 | 2 | null | 4612 | 9 | null | It depends on what you really want, (GMM, time series, panel...) but I can recommand those two books:
- Fumio Hayashi's "Econometrics" and
- Davidson and McKinnon "Econometric Theory and Methods".
For a course in econometric time series, Hamilton's ["Time Serie Analysis"](https://press.princeton.edu/books/hardcover... | null | CC BY-SA 4.0 | null | 2010-11-17T08:02:48.417 | 2021-02-14T17:18:42.730 | 2021-02-14T17:18:42.730 | 53690 | 2028 | null |
4619 | 1 | null | null | 2 | 253 | I have two surveys of two separate populations (I don't know that they are necessarily distinct, but they are from two different databases) that ask a similar set of questions. Some questions are basic demographics (e.g. age, income), while other questions are a bit more detailed or about their opinions (e.g. brand pr... | Establishing that the population sampled of two separate surveys is the same | CC BY-SA 2.5 | 0 | 2010-11-17T08:08:19.927 | 2010-11-18T13:06:58.253 | 2010-11-17T14:37:06.837 | 930 | 1195 | [
"psychometrics",
"survey"
] |
4620 | 1 | null | null | 3 | 4977 | Say, for example, that I want to determine the market share or relative popularity of coffee houses within a certain population through a survey. What is the best way to write a question that accurately measures this?
Some issues that I am thinking of:
I don't want to ask a single choice question ("Which coffee house ... | Determining market share from multiple choice questions on a survey | CC BY-SA 2.5 | null | 2010-11-17T08:36:48.267 | 2010-11-17T18:47:25.040 | null | null | 1195 | [
"survey"
] |
4621 | 2 | null | 4608 | 43 | null | To get the gradient we differentiate the loss with respect to $i$th component of $w$.
Rewrite hinge loss in terms of $w$ as $f(g(w))$ where $f(z)=\max(0,1-y\ z)$ and $g(w)=\mathbf{x}\cdot \mathbf{w}$
Using chain rule we get
$$\frac{\partial}{\partial w_i} f(g(w))=\frac{\partial f}{\partial z} \frac{\partial g}{\partial... | null | CC BY-SA 3.0 | null | 2010-11-17T09:25:37.863 | 2013-01-29T22:28:16.393 | 2013-01-29T22:28:16.393 | -1 | 511 | null |
4622 | 2 | null | 4612 | 6 | null | I would definitely recommend M. Verbeek's [A Guide to Modern Econometrics](https://rads.stackoverflow.com/amzn/click/com/0471899828).
Woolwridge is too wordy (and this long-windedness loses the reader's focus too early in the chapters). Greene (i'm referring to the 5th edition) often gets lost in minutiae: i.e. strives... | null | CC BY-SA 4.0 | null | 2010-11-17T09:41:47.610 | 2021-02-14T19:01:17.233 | 2021-02-14T19:01:17.233 | 603 | 603 | null |
4623 | 1 | null | null | 6 | 841 | I have a 3-dimensional sample $(X_k,Y_k,Z_k), k=1, \ldots, N$ which I suspect to be uniform on some parallelepiped in $R^3$ (i.e. a set of the form [a;b]X[c;d]X[e;f], where numbers a,b,c,d,e,f are unknown).
- How should I estimate numbers a, b, c, d, e, f? Obviously I can try MLE, but then my estimates are biased. Doe... | How to check that a sample suits multi-dimensional uniform distribution? | CC BY-SA 2.5 | null | 2010-11-17T10:16:58.917 | 2010-11-17T13:14:30.247 | 2010-11-17T12:24:02.010 | 8 | null | [
"distributions",
"hypothesis-testing",
"estimation"
] |
4624 | 1 | 4626 | null | 12 | 994 | I am fitting an L1-regularized linear regression to a very large dataset (with n>>p.) The variables are known in advance, but the observations arrive in small chunks. I would like to maintain the lasso fit after each chunk.
I can obviously re-fit the entire model after seeing each new set of observations. This, however... | Updating the lasso fit with new observations | CC BY-SA 2.5 | null | 2010-11-17T10:33:27.813 | 2016-02-17T15:13:51.587 | 2010-11-17T15:53:54.630 | 439 | 439 | [
"regression",
"lasso"
] |
4625 | 2 | null | 4609 | 4 | null | PASW 18 does give you the test statistic in addition to the p value.
For example, if you have selected Mann Whitney test, the output from SPSS will include a Test Statistics box that shows the mann Whitney U statistic
The same thing applies for the Kruskal Wallis test, although note that SPSS labels the statistic Chi-... | null | CC BY-SA 2.5 | null | 2010-11-17T10:47:04.823 | 2010-11-17T10:47:04.823 | null | null | 2030 | null |
4626 | 2 | null | 4624 | 7 | null | The lasso is fitted through LARS (an iterative process, that starts at some initial estimate $\beta^0$). By default $\beta^0=0_p$ but you can change this in most implementation (and replace it by the optimal $\beta^*_{old}$ you already have). The closest $\beta^*_{old}$ is to $\beta_{new}^*$, the smaller the number of ... | null | CC BY-SA 3.0 | null | 2010-11-17T10:59:33.970 | 2016-02-17T15:13:51.587 | 2016-02-17T15:13:51.587 | 603 | 603 | null |
4628 | 2 | null | 4612 | 6 | null | I really like Kennedy's A Guide to Econometrics, which is unusual in its setup, since every topic is discussed on three different levels, first in a non-technical way, then going into details of application and finally going into theoretical details, although the theoretical parts are a bit superficial.
| null | CC BY-SA 2.5 | null | 2010-11-17T11:51:36.763 | 2010-11-17T11:51:36.763 | null | null | 1766 | null |
4629 | 2 | null | 4551 | 7 | null | In psychology, the cardinal sin (for me) is the use of principal components analysis to examine the hypothesised latent structure underlying a psychometric test.
Not testing for normality before using tests which assume this.
| null | CC BY-SA 2.5 | null | 2010-11-17T12:03:29.180 | 2010-11-17T12:03:29.180 | null | null | 656 | null |
4630 | 2 | null | 4623 | 2 | null |
- For the 1D continuous uniform distribution U(a,b) the uniformly minimum variance unbiased (UMVU) estimates of a and b can be obtained in closed form by a straightforward example of maximum spacing estimation. Can't see any reason that applying this separately for each dimension wouldn't give you UMVU estimates of al... | null | CC BY-SA 2.5 | null | 2010-11-17T13:14:30.247 | 2010-11-17T13:14:30.247 | null | null | 449 | null |
4631 | 2 | null | 4620 | 1 | null | Frequency show the values that variables take in a sample.
In other words, shows the amount of individuals said that prefer coffeehouse A. This amount would be the frequency of coffeehouse A.
axis:
X:coffeehouses
Y:amount of individuals
| null | CC BY-SA 2.5 | null | 2010-11-17T13:42:36.827 | 2010-11-17T13:42:36.827 | null | null | 1746 | null |
4632 | 2 | null | 4612 | 3 | null | One at a somewhat lower level of mathematical sophistication than Wooldridge (less dense, more pictures), but a bit more up to date on some of the fast-moving areas:
Murray, Michael P. Econometrics: A Modern Introduction. Addison Wesley, 2006. 976 pp. ISBN [9780321113610](http://en.wikipedia.org/w/index.php?title=Speci... | null | CC BY-SA 2.5 | null | 2010-11-17T13:49:06.863 | 2010-11-17T13:49:06.863 | null | null | 449 | null |
4633 | 2 | null | 4610 | 5 | null | As @onestop writes, the GM and GSD are natural parameters for a lognormal distribution. However, they can be estimated from the arithmetic mean ($\mu$) and (usual) SD ($\sigma$) just by solving the [formulas](http://en.wikipedia.org/wiki/Log-normal_distribution)
$$\mu = \exp(\nu+ \tau^2/2) \text{ and}$$
$$\sigma^2 = ... | null | CC BY-SA 2.5 | null | 2010-11-17T14:02:40.130 | 2010-11-17T14:02:40.130 | null | null | 919 | null |
4634 | 2 | null | 4612 | 4 | null | "[Applied Econometrics with R](http://www.springer.com/economics/econometrics/book/978-0-387-77316-2)" (Kleiber, Zeileis 2008) is a good introduction using R, and is accompanied by the [AER package](http://cran.r-project.org/web/packages/AER/index.html).
| null | CC BY-SA 2.5 | null | 2010-11-17T14:15:22.657 | 2010-11-17T14:15:22.657 | null | null | 5 | null |
4635 | 2 | null | 4612 | 9 | null | "[Mostly Harmless Econometrics: An Empiricist's Companion](http://rads.stackoverflow.com/amzn/click/0691120358)" (Angrist, Pischke 2008) is a less technical and entertaining summary of the field. I wouldn't describe it as a beginner book, but it's well worth reading once you understand the basics.
| null | CC BY-SA 2.5 | null | 2010-11-17T14:17:55.447 | 2010-11-17T16:30:37.287 | 2010-11-17T16:30:37.287 | 5 | 5 | null |
4636 | 2 | null | 4620 | 1 | null | You can ask consumers a question along the following lines:
>
Out of every 100 visits to a coffee house how many times do you visit each one of the following coffee house? Please ensure that the total adds up to 100.
A. Option 1
B. Option 2.. etc
You can then normalize to get percentage of times each consumer goes t... | null | CC BY-SA 2.5 | null | 2010-11-17T14:21:11.723 | 2010-11-17T14:21:11.723 | 2020-06-11T14:32:37.003 | -1 | null | null |
4637 | 2 | null | 4620 | 3 | null | I'd suggest several Qs along these lines:
- Which is the one coffee house you go to most often?
- What other coffee houses do you visit more than once a month (say)? [Probe to negative, i.e. keep asking ".. and which others do you visit more than once a month?" ".. and which others?" until interviewee answers "none"/... | null | CC BY-SA 2.5 | null | 2010-11-17T14:21:31.443 | 2010-11-17T14:21:31.443 | null | null | 449 | null |
4638 | 2 | null | 4619 | 3 | null | I think that for subject-specific characteristics, like demographic data, you can proceed the usual way (t-test, etc.). This will help showing that your samples don't differ according to these variables. About self-reported attitude data, if you have very few items, skip to step 2, otherwise step 1 might be appropriate... | null | CC BY-SA 2.5 | null | 2010-11-17T14:36:48.460 | 2010-11-17T18:22:13.840 | 2010-11-17T18:22:13.840 | 930 | 930 | null |
4639 | 1 | 4644 | null | 21 | 61614 | In R, the `drop1`command outputs something neat.
These two commands should get you some output:
`example(step)#-> swiss`
`drop1(lm1, test="F")`
Mine looks like this:
```
> drop1(lm1, test="F")
Single term deletions
Model:
Fertility ~ Agriculture + Examination + Education + Catholic +
Infant.Mortality
... | Interpreting the drop1 output in R | CC BY-SA 2.5 | null | 2010-11-17T15:59:25.153 | 2019-08-01T17:16:21.440 | 2010-11-18T10:53:37.813 | 1994 | 1994 | [
"r",
"regression",
"self-study",
"stepwise-regression"
] |
4640 | 1 | null | null | 11 | 38886 | In R, the `step` command is supposedly intended to help you select the input variables to your model, right?
The following comes from
`example(step)#-> swiss` &
`step(lm1)`
```
> step(lm1)
Start: AIC=190.69
Fertility ~ Agriculture + Examination + Education + Catholic +
Infant.Mortality
Df ... | Interpreting the step output in R | CC BY-SA 3.0 | null | 2010-11-17T16:24:18.900 | 2017-03-08T11:22:22.217 | 2012-11-13T05:51:04.160 | 16705 | 1994 | [
"r",
"self-study",
"stepwise-regression"
] |
4641 | 2 | null | 4639 | 13 | null | `drop1` gives you a comparison of models based on the AIC criterion, and when using the option `test="F"` you add a "type II ANOVA" to it, as explained in the help files. As long as you only have continuous variables, this table is exactly equivalent to `summary(lm1)`, as the F-values are just those T-values squared. ... | null | CC BY-SA 2.5 | null | 2010-11-17T16:28:20.420 | 2010-11-17T17:07:15.003 | 2010-11-17T17:07:15.003 | 1124 | 1124 | null |
4642 | 1 | 4654 | null | 9 | 1890 | I am familiar with meta analysis and meta regression techniques (using the R package `metafor` from Viechtbauer), but I recently stumbled on a problem I can't easily solve. Say we have a disease that can go from mother to the unborn child, and it has been studied already a number of times. Mother and child were tested ... | Meta analysis on studies with 0-frequency cells | CC BY-SA 2.5 | null | 2010-11-17T17:17:21.297 | 2012-07-29T13:50:27.307 | 2010-11-17T23:12:37.027 | 1124 | 1124 | [
"meta-analysis",
"odds-ratio",
"relative-risk"
] |
4643 | 1 | 4646 | null | 5 | 214 | I originally asked this on a machine learning site, but one of the responses made me think that maybe this site is more suitable.
Suppose you have two weighted coins, and every day you flip each one a number of times and record the total number of heads. So on the tenth day you might have flipped coin A 106 times, coin... | Toy regression question with latent variables | CC BY-SA 2.5 | null | 2010-11-17T18:06:56.853 | 2010-11-18T14:25:57.537 | 2010-11-18T14:25:57.537 | 919 | 1777 | [
"regression",
"latent-variable"
] |
4644 | 2 | null | 4639 | 6 | null | For reference, these are the values that are included in the table:
`Df` refers to [Degrees of freedom](http://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)), "the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary."
The `Sum of Sq` column refers... | null | CC BY-SA 4.0 | null | 2010-11-17T18:07:33.793 | 2019-08-01T17:16:21.440 | 2019-08-01T17:16:21.440 | -1 | 1994 | null |
4645 | 2 | null | 4620 | 2 | null | I think there is a big difference (both practical and how you approach the problem) in popularity vis a vis market share. Since market share is more analytically challenging, I'll focus on that.
In my opinion, the best solution to this problem is going to involve a set of stated preference experiments, more formally k... | null | CC BY-SA 2.5 | null | 2010-11-17T18:47:25.040 | 2010-11-17T18:47:25.040 | null | null | 696 | null |
4646 | 2 | null | 4643 | 5 | null | Let's take the second question first: in most cases the presence of C makes the parameters unidentifiable and there's no way to estimate anything. Constraining the intercept to be positive won't help at all.
The first problem can be solved by Maximum Likelihood. After all, writing $p$ for the probability that a flip ... | null | CC BY-SA 2.5 | null | 2010-11-17T20:56:40.703 | 2010-11-18T14:22:23.357 | 2010-11-18T14:22:23.357 | 919 | 919 | null |
4647 | 2 | null | 4642 | 6 | null | Usually 0's imply that you have to use exact methods instead of relying on asymptotical methods such as meta-analysis with odds ratios. If you are willing to assume that the study effect is fixed, an exact Maentel-Hanszel test is the way to go. For an exact random effects analysis, you have to use a binomial regression... | null | CC BY-SA 2.5 | null | 2010-11-17T21:12:39.227 | 2010-11-23T21:43:38.687 | 2010-11-23T21:43:38.687 | 279 | 279 | null |
4648 | 2 | null | 4640 | 2 | null | The part of the printout at the end is the model you are left with. You can also get it if you capture the value of the `step` function:
```
final.mod <- step(lm1)
final.mod
```
| null | CC BY-SA 2.5 | null | 2010-11-17T21:16:43.250 | 2010-11-17T21:16:43.250 | null | null | 279 | null |
4649 | 1 | null | null | 1 | 3351 | If the outcome of a market could be expressed as a probability it might be:
Outcome - Description - Probability as a %
- Up a lot 20% (a move of say more than 10%)
- Down a lot 20%
- Up a bit 20% (a move of between 0 and 10%)
- Down a bit 20%
- Sideways 20%
So the probability of any single outcome is 1/5 o... | How to combine probabilities? | CC BY-SA 2.5 | null | 2010-11-17T21:17:44.603 | 2010-11-17T22:22:02.307 | 2010-11-17T21:53:57.087 | null | null | [
"probability"
] |
4650 | 2 | null | 4649 | 2 | null | OK, let me say something about the multinomial distribution since I brought it up.
Suppose I have 2 dice both with 5 faces each. Assuming that the dice are fair, the probability of one particular face turning up with one die is indeed $1/5$ or 20%.
Now, let's ask what happens when we throw two dice and ask ourselves wh... | null | CC BY-SA 2.5 | null | 2010-11-17T21:36:42.957 | 2010-11-17T22:22:02.307 | 2010-11-17T22:22:02.307 | 2036 | 2036 | null |
4652 | 1 | null | null | 7 | 3559 | I'm trying to run a basic gradient descent algorithm with a absolute loss function. I can get it to converge to a good solution by it requires a much lower step size and more iterations than had I used square loss. Is this normal? Should I expect absolute loss to take a longer time to come to a good solution or potenti... | Gradient descent oscillating a lot. Have I chosen my step direction incorrectly? | CC BY-SA 2.5 | null | 2010-11-17T23:10:31.310 | 2010-11-19T05:14:16.130 | 2010-11-19T05:03:38.713 | 2023 | 2023 | [
"optimization",
"loss-functions"
] |
4654 | 2 | null | 4642 | 5 | null | Seems to me this is one of the rare situations where it might well be better to meta-analyse risk differences rather than risk ratios or odds ratios. The risk difference $P(Kid_+ | Mum_+) - P(Kid_+|Mum_-)$ is estimated in each study by $D/(B+D) - C/(A+C)$. That should be finite in all studies even when $C=0$, so there... | null | CC BY-SA 2.5 | null | 2010-11-18T00:25:36.700 | 2010-11-18T00:36:46.683 | 2010-11-18T00:36:46.683 | 449 | 449 | null |
4655 | 1 | 4657 | null | 10 | 3082 | Background
notation: RV= random variable, $\mu=$ mean $m=$ median
Jensen's Inequality considers the relationship between the mean of a function of an RV and the function of the mean of an RV.
If $f(x)$ strictly convex:
$$\mu (f(x)) > f(\mu (x))\mathrm{\hspace{20mm}(1)}$$
Conversely if -f(x) is strictly convex:
$$\mu ... | Is there a relationship between the median of a function of random variables and the function of the median of random variables? | CC BY-SA 2.5 | null | 2010-11-18T00:37:37.360 | 2010-12-01T05:56:23.050 | 2010-11-18T15:59:28.850 | 1381 | 1381 | [
"probability",
"mean",
"random-variable",
"mathematical-statistics",
"median"
] |
4656 | 2 | null | 4652 | 5 | null | When you say 'a absolute loss function', do you mean you're using [least absolute deviations](http://en.wikipedia.org/wiki/Least_absolute_deviations) (LAD) instead of the more usual ordinary least squares (OLS)? As that wikipedia article says, although LAD is more robust to outliers than OLS it can be unstable and even... | null | CC BY-SA 2.5 | null | 2010-11-18T00:49:28.300 | 2010-11-18T00:49:28.300 | null | null | 449 | null |
4657 | 2 | null | 4655 | 5 | null | Let the cdf of $x$ be denoted by $F_X(x)$. Thus, the median of $X$ denoted by $m_x$ satisfies:
$F_X(m_x)=0.5$
Consider $Y = X^2$. Thus, the cdf of $Y$ is given by:
$P(Y \le y) = P(X^2 \le y)$
In other words, the cdf of $Y$ is given by:
$F_Y(y) = F_X(\sqrt{y}) - F_X(-\sqrt{y})$
The median for $Y$ denoted by $m_Y$ sat... | null | CC BY-SA 2.5 | null | 2010-11-18T01:09:48.170 | 2010-11-18T01:09:48.170 | null | null | null | null |
4658 | 1 | 4664 | null | 13 | 5869 | I have two set of data that are roughly centered around zero but I suspect that they have different tails.
I know a few tests to compare the distribution to a normal distribution, but I would like to compare directly the two distributions.
Is there a simple test to compare the fatness of tail of 2 distributions?
Thank... | Comparison of the tails of two sample distributions | CC BY-SA 2.5 | null | 2010-11-18T01:52:10.523 | 2019-09-14T01:10:45.783 | 2019-09-14T01:10:45.783 | 7290 | 1709 | [
"hypothesis-testing",
"distributions",
"kurtosis",
"fat-tails"
] |
4659 | 1 | 4684 | null | 45 | 40623 | I'm more of a programmer than a statistician, so I hope this question isn't too naive.
It happens in sampling program executions at random times. If I take N=10 random-time samples of the program's state, I could see function Foo being executed on, for example, I=3 of those samples. I'm interested in what that tells me... | Relationship between Binomial and Beta distributions | CC BY-SA 2.5 | null | 2010-11-18T02:51:42.343 | 2020-05-13T06:45:12.307 | 2010-11-20T14:59:45.877 | 1270 | 1270 | [
"binomial-distribution",
"beta-binomial-distribution",
"beta-distribution"
] |
4660 | 2 | null | 4658 | 2 | null | The Chi Square test (Goodness-of-Fit test) will be very good at comparing the tails of two distributions since it is structured to compare two distributions by buckets of values (graphically represented by a histogram). And, the tails will consist in the far most buckets.
Even though this test focuses on the whole dis... | null | CC BY-SA 2.5 | null | 2010-11-18T03:06:39.537 | 2010-11-18T19:16:24.867 | 2010-11-18T19:16:24.867 | 1329 | 1329 | null |
4661 | 2 | null | 4658 | 2 | null | How about fitting the [generalized lambda distribution](http://tolstoy.newcastle.edu.au/~rking/gld/) and bootstrapping confidence intervals on the 3rd and 4th parameters?
| null | CC BY-SA 2.5 | null | 2010-11-18T03:47:12.950 | 2010-11-18T03:47:12.950 | null | null | 364 | null |
4662 | 2 | null | 4640 | 7 | null | The last step table is indeed the end result of the "stepwise regression". The caveat here is that usually you don't want to use this approach when there is a principled way to approach your model specification. The call is the lm call which would produce the equation used in the final step. Coefficients are the act... | null | CC BY-SA 3.0 | null | 2010-11-18T06:46:07.077 | 2017-03-08T11:22:22.217 | 2017-03-08T11:22:22.217 | 138249 | 196 | null |
4663 | 1 | 4790 | null | 41 | 20236 | Least-angle regression and the lasso tend to produce very similar regularization paths (identical except when a coefficient crosses zero.)
They both can be efficiently fit by virtually identical algorithms.
Is there ever any practical reason to prefer one method over the other?
| Least-angle regression vs. lasso | CC BY-SA 2.5 | null | 2010-11-18T07:28:22.207 | 2019-05-24T13:57:33.993 | null | null | 439 | [
"regression",
"lasso"
] |
4664 | 2 | null | 4658 | 6 | null | This question seems to belong to the same family as [this earlier one about testing whether two samples have the same skew](https://stats.stackexchange.com/q/1853/449), so [you may like to read my answer to that](https://stats.stackexchange.com/questions/1853/testing-two-independent-samples-for-null-of-same-skew/1953#1... | null | CC BY-SA 2.5 | null | 2010-11-18T08:46:46.627 | 2010-11-18T12:45:27.967 | 2017-04-13T12:44:25.243 | -1 | 449 | null |
4666 | 2 | null | 4663 | -1 | null | In some contexts a regularized version of the least squares solution may be preferable. The LASSO (least absolute shrinkage and selection operator) algorithm, for example, finds a least-squares solution with the constraint that | β | 1, the L1-norm of the parameter vector, is no greater than a given value. Equivalently... | null | CC BY-SA 2.5 | null | 2010-11-18T09:46:57.010 | 2010-11-18T09:46:57.010 | null | null | 1808 | null |
4667 | 1 | 4672 | null | 6 | 222 | Through a project I am now working on (which I won't link to so to not have this an ad question), I came to realize how difficult it is to find R resources, not in English.
Thus my question is - what resources do you know of, recommend, for learning R in non-English languages? (tutorials, blogs, wiki's, forums, and so... | R resources in non-English languages | CC BY-SA 2.5 | null | 2010-11-18T09:59:09.933 | 2011-07-02T20:04:11.583 | 2011-07-02T20:04:11.583 | null | 253 | [
"r",
"references"
] |
4669 | 2 | null | 4667 | 3 | null | All RSS feeds I follow are in English actually, so I'll just point to tutorials available in French, or made by French researchers.
Apart from the [Contributed Documentation](http://cran.r-project.org/other-docs.html) on CRAN, I often browse the R website hosted at the [bioinformatics lab](http://pbil.univ-lyon1.fr/R/e... | null | CC BY-SA 2.5 | null | 2010-11-18T10:10:55.083 | 2010-11-18T10:10:55.083 | null | null | 930 | null |
4670 | 2 | null | 4667 | 4 | null | There doesn't appear to be much in Russian, but here is a couple of links:
- http://herba.msu.ru/shipunov/software/r/r-ru.htm contains pointers to a number of Russian-language R resources;
- http://voliadis.ru/taxonomy/term/18 is a blog with some R content.
| null | CC BY-SA 2.5 | null | 2010-11-18T10:36:54.880 | 2010-11-18T10:36:54.880 | null | null | 439 | null |
4671 | 1 | 4673 | null | 1 | 620 | I am using an automatic model selection procedure, "step". The model of depart (the biggest possible) is a polynom, say of the 4th degree.
```
Depart<-lm(y~x+I(x^2)+I(x^3)+I(x^4))
Final<-step(Depart)
```
I need to transform the Final model to a corresponding function. How can i do this?
| R: How to create a function from a model? | CC BY-SA 2.5 | null | 2010-11-18T10:46:34.230 | 2010-11-18T12:00:38.503 | null | null | 2043 | [
"r",
"stepwise-regression"
] |
4672 | 2 | null | 4667 | 5 | null | In german:
- A short introduction to R very short, covers only the basics of R programming
- http://de.wikibooks.org/wiki/GNU_R teaches the basics of R programmming in detail and also contains some examples of producing graphics and statistics.
- cran.r-project.org/doc/contrib/Sawitzki-Einfuehrung.pdf a lengthy int... | null | CC BY-SA 2.5 | null | 2010-11-18T10:57:43.987 | 2010-11-18T10:57:43.987 | null | null | 264 | null |
4673 | 2 | null | 4671 | 8 | null | Do you mean something like this:
`f<-function(newdata)predict(Final,data.frame(x=newdata))`
?
| null | CC BY-SA 2.5 | null | 2010-11-18T12:00:38.503 | 2010-11-18T12:00:38.503 | null | null | 439 | null |
4674 | 2 | null | 4667 | 3 | null | Here is a german blog with some posts on R:
[http://blog.berndweiss.net/tag/r/](http://blog.berndweiss.net/tag/r/)
Recently started, with no posts on R yet, but focused on open data, is this blog:
[http://blog.zeit.de/open-data](http://blog.zeit.de/open-data)
| null | CC BY-SA 2.5 | null | 2010-11-18T12:14:32.640 | 2010-11-18T12:14:32.640 | null | null | 573 | null |
4675 | 2 | null | 4667 | 4 | null | Some german blog entries:
[http://www.schockwellenreiter.de/blog/tag/r/](http://www.schockwellenreiter.de/blog/tag/r/)
and
[http://markheckmann.wordpress.com/category/r-r-code/](http://markheckmann.wordpress.com/category/r-r-code/)
edit: and one more:
[http://wagezudenken.blogspot.com/](http://wagezudenken.blogspot.com... | null | CC BY-SA 3.0 | null | 2010-11-18T12:42:26.417 | 2011-07-01T22:17:04.383 | 2011-07-01T22:17:04.383 | 1050 | 1050 | null |
4676 | 2 | null | 4619 | 2 | null | To add to chl's answer, another step you can take to ensure your data is representative of the population as a whole is to compare both samples to a third party data set. In the United States, there is the [American Community Survey](http://factfinder.census.gov/servlet/DatasetMainPageServlet?_program=ACS&_submenuId=da... | null | CC BY-SA 2.5 | null | 2010-11-18T13:06:58.253 | 2010-11-18T13:06:58.253 | null | null | 696 | null |
4678 | 2 | null | 4454 | 4 | null | As you and Matt Parker both noted, there can be a big difference in the preparation of a survey script that is digested by your client and how you prepare the script for your programmers. In a professional setting, "client" friendly scripts generally win focus and the programmers are left to put the pieces together as ... | null | CC BY-SA 2.5 | null | 2010-11-18T13:20:41.933 | 2010-11-18T13:20:41.933 | null | null | 696 | null |
4679 | 2 | null | 4659 | 14 | null | Look at the pdf of Binomial as a function of $x$: $$f(x) = {n\choose{x}}p^{x}(1-p)^{n-x}$$ and the pdf of Beta as a function of $p$: $$g(p)=\frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)}p^{a-1}(1-p)^{b-1}$$
You probably can see that with an appropriate (integer) choice for $a$ and $b$ these are the same. As far as I can tel... | null | CC BY-SA 2.5 | null | 2010-11-18T14:03:15.770 | 2010-11-18T14:03:15.770 | null | null | 279 | null |
4680 | 2 | null | 4454 | 4 | null | I wrote a post some time ago about how to use Google spreadsheets + google forms + R for easily collecting and sharing data. It might prove useful to you or others:
[http://www.r-statistics.com/2010/03/google-spreadsheets-google-forms-r-easily-collecting-and-importing-data-for-analysis/](http://www.r-statistics.com/20... | null | CC BY-SA 2.5 | null | 2010-11-18T14:18:18.587 | 2010-11-18T14:18:18.587 | null | null | 253 | null |
4681 | 2 | null | 2988 | 4 | null | Following on from the post by Stephan Kolassa (I can't add this as a comment), I have some alternative code for a simulation. This uses the same basic structure, but is exploded a bit more, so perhaps it is a little easier to read. It also is based on the code by [Kleinman and Horton](http://sas-and-r.blogspot.com/20... | null | CC BY-SA 2.5 | null | 2010-11-18T14:47:29.653 | 2010-12-03T11:40:53.517 | 2010-12-03T11:40:53.517 | 1991 | 1991 | null |
4684 | 2 | null | 4659 | 44 | null | Consider the order statistics $x_{[0]} \le x_{[1]} \le \cdots \le x_{[n]}$ of $n+1$ independent draws from a uniform distribution. Because [order statistics have Beta distributions](http://en.wikipedia.org/wiki/Order_statistic#The_order_statistics_of_the_uniform_distribution), the chance that $x_{[k]}$ does not exceed... | null | CC BY-SA 2.5 | null | 2010-11-18T15:51:22.313 | 2010-11-18T23:03:39.207 | 2010-11-18T23:03:39.207 | 919 | 919 | null |
4685 | 1 | 4728 | null | 4 | 6894 | What is the difference between soft and hard expectation maximization?
EDIT: ok, i've found out this paper: [http://ttic.uchicago.edu/~dmcallester/ttic101-07/lectures/em/em.pdf](http://ttic.uchicago.edu/~dmcallester/ttic101-07/lectures/em/em.pdf)
that explain quite well the situations
| Soft and Hard EM (Expectation Maximization) | CC BY-SA 2.5 | null | 2010-11-18T16:19:43.160 | 2013-11-21T22:27:54.580 | 2010-11-19T11:20:52.973 | 2046 | 2046 | [
"fitting",
"expectation-maximization",
"unsupervised-learning"
] |
4686 | 1 | null | null | 5 | 307 | Suppose that I have a population, each represented by a bit $b_i$ for $i \in \{1,\ldots, n\}$. I would like to compute an estimate $\hat{B}$ of the parameter $B = \sum_{i=1}^nb_i$ so that with high probability, the error $|\hat{B}-B| \leq k$ for some fixed $k$. However, I have to pay a cost $c_i$ to sample bit $b_i$, a... | Sampling with non-uniform costs | CC BY-SA 2.5 | null | 2010-11-18T16:48:34.630 | 2010-11-22T15:43:17.257 | 2010-11-18T23:06:13.260 | 919 | null | [
"sample-size",
"sampling"
] |
4687 | 1 | 5693 | null | 5 | 2436 | I am trying to get a deeper understanding of the various types of Bayesian networks.
Most of the literature/lectures I've come across use discrete random variables exclusively and only mention continuous random variables in passing.
It seems if you want to mix discrete and continuous variables in a hybrid network, t... | Specifying conditional probabilities in hybrid Bayesian networks | CC BY-SA 2.5 | null | 2010-11-18T17:32:43.533 | 2016-05-01T20:18:57.380 | 2016-05-01T20:18:57.380 | 7290 | 1474 | [
"bayesian",
"random-variable",
"graphical-model",
"conditional-probability",
"prior"
] |
4688 | 2 | null | 4603 | 2 | null | I've just come across [mlpy](https://mlpy.fbk.eu/), which also has an implementation of the lasso (in Python.)
| null | CC BY-SA 2.5 | null | 2010-11-18T17:39:55.700 | 2010-11-18T17:39:55.700 | null | null | 439 | null |
4689 | 1 | 4690 | null | 27 | 19170 | What are the differences between generative and discriminative (discriminant) models (in the context of Bayesian learning and inference)?
and what it is concerned with prediction, decision theory or unsupervised learning?
| Generative vs discriminative models (in Bayesian context) | CC BY-SA 3.0 | null | 2010-11-18T18:16:48.990 | 2017-06-26T09:17:14.060 | 2017-06-26T09:17:14.060 | 3277 | 2046 | [
"bayesian",
"predictive-models",
"unsupervised-learning"
] |
4690 | 2 | null | 4689 | 39 | null | Both are used in supervised learning where you want to learn a rule that maps input x to output y, given a number of training examples of the form $\{(x_i,y_i)\}$. A generative model (e.g., naive Bayes) explicitly models the joint probability distribution $p(x,y)$ and then uses the Bayes rule to compute $p(y|x)$. On th... | null | CC BY-SA 3.0 | null | 2010-11-18T19:01:08.403 | 2017-06-26T09:09:48.467 | 2017-06-26T09:09:48.467 | 73177 | 881 | null |
4691 | 1 | null | null | 4 | 7977 | My teacher is asking whether it is possible to look at the Cronbachs's alpha when looking at the internal reliability of an ordinal scale. She thinks, because you use the mean of it, it is not possible, but I've seen it in previous research as well. I think it is possible, but I cannot give a technical explanation for... | Internal reliability for an ordinal scale | CC BY-SA 3.0 | null | 2010-11-18T20:40:16.053 | 2022-05-18T11:20:05.217 | 2011-10-04T07:02:14.510 | 930 | null | [
"self-study",
"reliability",
"scales",
"psychometrics"
] |
4693 | 2 | null | 4686 | 0 | null | If the costs $c_i$ are known a priori, it seems like a greedy sampling would give you some guarantees. That is, sample the $n-2k$ bits in order of increasing cost. This gives a $k$-error guarantee on $B$ with probability $1$ in the obvious way. I am curious if this strategy is the limit strategy of some sane sequence o... | null | CC BY-SA 2.5 | null | 2010-11-18T21:14:38.350 | 2010-11-20T05:09:34.580 | 2010-11-20T05:09:34.580 | 795 | 795 | null |
4694 | 1 | 4705 | null | 2 | 1507 | [Here](http://www.ambion.com/techlib/tn/95/954.html) is an example of hierarchical clustering of genes in the microarray data using the weighted pair gene method in `Spotfire`. I am not sure how to do this in `R`. In the `hclust` function, I see `ward", "single", "complete", "average", "mcquitty", "median" or "centroid... | How to do weighted pair hierarchical clustering in R? | CC BY-SA 2.5 | null | 2010-11-18T21:49:01.410 | 2010-11-19T07:43:58.327 | null | null | 1307 | [
"r",
"clustering",
"microarray"
] |
4695 | 1 | null | null | 6 | 484 | Suppose I have time series observations from distributions drawn from some population. That is, I observe $X_{t,i}$ for $t=1,2,...,T,$ and $i=1,2,...,n$, where I believe that $X_{t,i}$ have pdf $f(\theta_i)$. (I have some idea about the distribution of the $\theta_i$, but that may not be important here.) I have some sa... | How to test for parameter stationarity? | CC BY-SA 2.5 | null | 2010-11-18T22:11:04.460 | 2010-11-21T04:25:37.277 | null | null | 795 | [
"time-series",
"estimation",
"stationarity"
] |
4696 | 2 | null | 4691 | 6 | null | From a practical perspective, I don't see any obvious reason to not use Cronbach's alpha with ordinal items (e.g., Likert-type items), as is commonly done in most of the studies. It is a lower bound for reliability, and is essentially used as an indicator of internal consistency of a test or questionnaire. The usual as... | null | CC BY-SA 4.0 | null | 2010-11-18T22:15:45.467 | 2022-05-18T11:20:05.217 | 2022-05-18T11:20:05.217 | 79696 | 930 | null |
4697 | 2 | null | 4686 | 4 | null | Methods to find a solution are well known, but this is a messy problem. A tiny example reveals much, so consider the case $n = 2$. Let the cost of sampling bit 1 be $c_1 = 1$ and the cost of sampling bit 2 be $c_2 = c$. Without any loss of generality assume this is the expensive bit: $c \ge 1$.
Either we sample both... | null | CC BY-SA 2.5 | null | 2010-11-18T22:38:15.733 | 2010-11-18T22:44:36.383 | 2010-11-18T22:44:36.383 | 919 | 919 | null |
4698 | 1 | 5024 | null | 3 | 203 | I have two models $M_1$ and $M_2$ that I am using to try and compare to observed data $D$. $M_1$ is an $n_1$-dimensional model, and $M_2$ is an $n_2$-dimensional problem. The Bayes factor $K$ to compare the models can be calculated using:
$K = P(D|M_1)/P(D|M_2) $
assuming no prior preference for either model. The nume... | Bayesian model comparison for randomly sampled sets of models | CC BY-SA 2.5 | null | 2010-11-18T22:54:30.957 | 2010-11-30T09:33:50.900 | null | null | 2052 | [
"probability",
"bayesian",
"modeling"
] |
4699 | 2 | null | 4695 | 2 | null | This problem is encountered in quality control/[statistical process control](http://en.wikipedia.org/wiki/Statistical_process_control) settings. There's a large literature, as you have hinted, because different parameters as estimated in various ways from different forms of sampling different distributions can be expe... | null | CC BY-SA 2.5 | null | 2010-11-18T23:18:18.273 | 2010-11-18T23:18:18.273 | null | null | 919 | null |
4700 | 1 | 4702 | null | 414 | 831780 | In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect and mixed effect models?
| What is the difference between fixed effect, random effect and mixed effect models? | CC BY-SA 2.5 | null | 2010-11-19T00:03:28.163 | 2023-03-06T12:29:41.600 | 2010-11-19T07:58:26.983 | 930 | 1991 | [
"mixed-model",
"random-effects-model",
"definition",
"fixed-effects-model"
] |
4701 | 2 | null | 4659 | 5 | null | As you noted, the Beta distribution describes the distribution of the trial probability parameter $F$, while the binomial distribution describes the distribution of the outcome parameter $I$. Rewriting your question, what you asked about was why
$$P(F \le \frac {i+1} n)+P(I \le fn-1)=1$$
$$P(Fn \le i+1)+P(I+1 \le fn)... | null | CC BY-SA 2.5 | null | 2010-11-19T01:22:33.283 | 2010-11-19T03:52:36.887 | 2010-11-19T03:52:36.887 | 2456 | 2456 | null |
4702 | 2 | null | 4700 | 220 | null | Statistician Andrew Gelman [says that the terms 'fixed effect' and 'random effect' have variable meanings](http://www.stat.columbia.edu/%7Ecook/movabletype/archives/2005/01/why_i_dont_use.html) depending on who uses them. Perhaps you can pick out which one of the 5 definitions applies to your case. In general it may be... | null | CC BY-SA 3.0 | null | 2010-11-19T01:40:26.773 | 2016-09-27T04:40:45.413 | 2020-06-11T14:32:37.003 | -1 | 1146 | null |
4703 | 2 | null | 4700 | 48 | null | Fixed effect: Something the experimenter directly manipulates and is often repeatable, e.g., drug administration - one group gets drug, one group gets placebo.
Random effect: Source of random variation / experimental units e.g., individuals drawn (at random) from a population for a clinical trial.
Random effects estima... | null | CC BY-SA 2.5 | null | 2010-11-19T04:11:03.943 | 2010-11-19T04:11:03.943 | null | null | 966 | null |
4704 | 2 | null | 4652 | 2 | null | This is possibly a consequence of a known deficiency of steepest descent algorithms in general. Using a [conjugate gradient algorithm](http://en.wikipedia.org/wiki/Conjugate_gradient_method) may improve convergence.
| null | CC BY-SA 2.5 | null | 2010-11-19T05:14:16.130 | 2010-11-19T05:14:16.130 | null | null | 795 | null |
4705 | 2 | null | 4694 | 2 | null | About your first question, it seems that the `mcquitty` option corresponds to WPGMA clustering, while `average` is for UPGMA. It is just by looking at the [source code](http://svn.r-project.org/R/trunk/src/library/stats/R/hclust.R), so it is worth to double check it. But it also referred as is in the `upgma()` function... | null | CC BY-SA 2.5 | null | 2010-11-19T07:43:58.327 | 2010-11-19T07:43:58.327 | null | null | 930 | null |
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