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4154 | 2 | null | 4150 | 4 | null | You should google [EM algorithm](http://en.wikipedia.org/wiki/Expectation-maximization_algorithm). The wiki has a description of the algorithm with an example of the application of this algorithm to [gaussian mixtures](http://en.wikipedia.org/wiki/Expectation-maximization_algorithm#Example%3a_Gaussian_mixture). Perhaps... | null | CC BY-SA 2.5 | null | 2010-11-03T00:39:58.123 | 2010-11-03T00:45:52.243 | 2010-11-03T00:45:52.243 | 919 | null | null |
4155 | 1 | 4156 | null | 6 | 813 | I've been learning X12-ARIMA by looking at data from a friend's service company, and wondering how to model the capacity of the company. That is, if the company is limited by a particular resource to only be able to handle 1,000 customers a week, how do I keep my ARIMA model from happily predicting 1,200 customers next... | Saturation in ARIMA (et al) models? | CC BY-SA 2.5 | null | 2010-11-03T02:22:36.290 | 2011-03-04T20:38:15.360 | null | null | 1764 | [
"time-series"
] |
4156 | 2 | null | 4155 | 3 | null | If Y is customer demand, than you are observing X=min(Y,1000) due to resource constraints. The actual Y could be larger, but you never observe it. So if you fit a time series model to X, you can set the forecasts to min(F,1000) where F is the forecast from the time series model. I don't think there is a need to do anyt... | null | CC BY-SA 2.5 | null | 2010-11-03T03:01:37.603 | 2010-11-03T03:01:37.603 | null | null | 159 | null |
4157 | 1 | 4163 | null | 3 | 624 | I've really only seen EM used for mixtures where one can point out multiple modes visually - e.g, the classic mixture of gaussians example. I would like to use EM for a mixture of an empirically defined, sharply peaked distribution and something that is more uniform - does anyone have an idea as to how much confidence... | Using the EM Algorithm for unimodal distributions? | CC BY-SA 2.5 | null | 2010-11-03T03:18:12.013 | 2011-03-27T16:05:08.313 | 2011-03-27T16:05:08.313 | 919 | 1777 | [
"modeling",
"mixture-distribution",
"expectation-maximization"
] |
4158 | 2 | null | 3814 | 24 | null | Confusing p-values and effect size (i.e. stating my effect is large because I have a really tiny p-value).
Slightly different than Stephan's [answer](https://stats.stackexchange.com/questions/3814/how-to-annoy-a-statistical-referee/3817#3817) of excluding effect sizes but giving p-values. I agree you should give both (... | null | CC BY-SA 2.5 | null | 2010-11-03T04:01:10.777 | 2010-11-03T04:01:10.777 | 2017-04-13T12:44:54.643 | -1 | 1036 | null |
4163 | 2 | null | 4157 | 3 | null | There are two questions here: 1) how much confidence should you put in your model with peaked and flat components. 2) how much confidence should you put in the EM algorithm as a way to fit this model.
Question 1 has the same answers as any other model, e.g. a regression model with particular covariates or a factor anal... | null | CC BY-SA 2.5 | null | 2010-11-03T07:46:46.563 | 2010-11-03T07:46:46.563 | null | null | 1739 | null |
4164 | 2 | null | 4138 | 3 | null | I interpreted the question to ask the distribution of the maximal element of a multivariate normal. In this case, the CDF can be computed from the CDF of a multivariate normal. This usually doesn't have a nice solution (even in terms of the univariate normal CDF), however can be evaluated numerically. In R:
```
library... | null | CC BY-SA 2.5 | null | 2010-11-03T08:29:08.687 | 2010-11-04T12:13:54.883 | 2010-11-04T12:13:54.883 | 495 | 495 | null |
4165 | 1 | 4167 | null | 20 | 18281 | I wish to decide if I should take a course called "INTRODUCTION TO STOCHASTIC PROCESSES" which will be held next semester in my University.
I asked the lecturer how studying such a course would help me as a statistician, he said that since he comes from probability, he knows very little of statistics and doesn't know h... | How will studying "stochastic processes" help me as a statistician? | CC BY-SA 2.5 | null | 2010-11-03T08:57:40.697 | 2013-01-12T16:13:52.467 | 2010-11-03T10:10:14.340 | 183 | 253 | [
"probability",
"stochastic-processes"
] |
4166 | 2 | null | 4165 | 3 | null | A deep understanding of survival analysis requires knowledge of counting processes, martingales, Cox processes... See e.g. Odd O. Aalen, Ørnulf Borgan, Håkon K. Gjessing. Survival and event history analysis: a process point of view. Springer, 2008. [ISBN 9780387202877](http://en.wikipedia.org/wiki/Special%3aBookSources... | null | CC BY-SA 2.5 | null | 2010-11-03T10:35:34.100 | 2010-11-03T10:35:34.100 | null | null | 449 | null |
4167 | 2 | null | 4165 | 22 | null | Stochastic processes underlie many ideas in statistics such as time series, markov chains, markov processes, bayesian estimation algorithms (e.g., Metropolis-Hastings) etc. Thus, a study of stochastic processes will be useful in two ways:
- Enable you to develop models for situations of interest to you.
An exposure to... | null | CC BY-SA 2.5 | null | 2010-11-03T10:36:25.520 | 2010-11-03T10:42:13.277 | 2020-06-11T14:32:37.003 | -1 | null | null |
4168 | 2 | null | 4165 | 3 | null | The short answer probably is that all observable processes, which we may want to analyze with statistical tools, are stochastic processes, that is, they contain some element of randomness. The course will probably teach you the mathematics behind these stochastic processes, e. g. distribution functions, which will allo... | null | CC BY-SA 2.5 | null | 2010-11-03T10:42:41.717 | 2010-11-03T10:42:41.717 | null | null | 1766 | null |
4169 | 2 | null | 4157 | 0 | null | I have a [paper](http://www.thinkingaboutthinking.org/wp-content/uploads/2010/05/Lawrence_BRM_in_press.pdf) in press that explores application of EM to estimation of a Von Mises & uniform mixture in the circular domain. (The Von Mises is the circular analogue of a gaussian.)
| null | CC BY-SA 2.5 | null | 2010-11-03T11:38:25.917 | 2010-11-03T11:38:25.917 | null | null | 364 | null |
4170 | 2 | null | 192 | 10 | null | Arguably, the question is not very precise. Rather than enumerating all measures of association for $2\times 2$ tables, I shall concentrate on the way such measures may be constructed and how to select the one that is most appropriate with respect to hypothesis or constraints relevant to a cross-classification.
The ver... | null | CC BY-SA 4.0 | null | 2010-11-03T12:52:22.440 | 2020-11-05T10:10:06.100 | 2020-11-05T10:10:06.100 | 930 | 930 | null |
4171 | 2 | null | 4165 | 3 | null | Just for the sake of completeness, an IID sequence of random variables is also a stochastic process (a very simple one).
| null | CC BY-SA 2.5 | null | 2010-11-03T13:00:45.750 | 2010-11-03T13:00:45.750 | null | null | 247 | null |
4172 | 1 | null | null | 4 | 387 | I am new to the area of statistics and I am hoping you can suggest methods I may use. Sorry if this is long but I might as well be as clear as possible on my first post :)
What I am worried most is that I may miss out on assumptions and draw conclusions based on statistical tests that, in fact, cannot be applied to my ... | How to "prove" that new measurement tool & process gives same result as old? | CC BY-SA 2.5 | null | 2010-11-03T13:17:39.483 | 2016-07-22T06:11:08.683 | 2016-07-22T06:11:08.683 | 1352 | 1784 | [
"time-series",
"correlation",
"reliability",
"agreement-statistics",
"bland-altman-plot"
] |
4173 | 2 | null | 4157 | 3 | null | I have used various algorithms, including Bayesian approaches (and, I am sorry to confess, even Excel many years ago), to fit mixtures. When there is not a clear visual indication of the two (or more components) in the histogram, you can expect the likelihood function to be extremely flat--almost parabolic--near its p... | null | CC BY-SA 2.5 | null | 2010-11-03T14:28:21.180 | 2010-11-03T14:28:21.180 | null | null | 919 | null |
4174 | 1 | 4180 | null | 6 | 752 | At work we have a hardware device that is failing for some yet to be determined reason. I have been tasked to see if I can make this device not fail by making changes to its software driver. I have constructed a software test bench which iterates over the driver functions which I feel are most likely to cause the devic... | Estimating the probability that a software change fixed a problem | CC BY-SA 2.5 | null | 2010-11-03T14:52:52.013 | 2023-01-23T16:12:16.213 | 2017-11-03T13:50:16.927 | 101426 | 1786 | [
"hypothesis-testing",
"distributions",
"t-test",
"prediction-interval"
] |
4175 | 1 | 4324 | null | 2 | 2720 | I am trying to understand how I can use resampling techniques to compliment my pre-planned analyses. This is not homework. I have a 5 sided die. 30 subjects call a number (1-5) and then roll the die. If it matches it's a hit, if not it's a miss. Each subject does this 25 times.
N is the the number of trials (=25) and ... | Resampling, binomial, z- and t-test: help with real data | CC BY-SA 2.5 | null | 2010-11-03T15:07:30.070 | 2010-11-10T07:39:23.930 | 2010-11-10T07:39:23.930 | 930 | 1614 | [
"r",
"hypothesis-testing"
] |
4176 | 2 | null | 4174 | 4 | null | There are a few ways of doing this problem. The way I would tackle this problem is as follows.
The data you have comes from a [geometric](http://en.wikipedia.org/wiki/Geometric_distribution) distribution. That is, the number of [Bernoulli trials](http://en.wikipedia.org/wiki/Bernoulli_distribution) before a failure. Th... | null | CC BY-SA 2.5 | null | 2010-11-03T15:16:19.827 | 2010-11-03T15:16:19.827 | null | null | 8 | null |
4177 | 2 | null | 4165 | 9 | null | You need to be careful how you ask this question. Since you could substitute almost anything in place of stochastic processes and it would still be potentially useful. For example, a course in biology could help with biological statistical consultancy since you know more biology!
I presume that you have a choice of mod... | null | CC BY-SA 2.5 | null | 2010-11-03T15:23:45.933 | 2010-11-03T15:23:45.933 | null | null | 8 | null |
4178 | 2 | null | 4065 | 4 | null | Some time has passed and I think I might have a solution at hand. I will describe my approach briefly to give you the general idea. The code should be enough to figure out the details. I like to attach code here, but it is a lot and stackexchange makes it not easy to do so. I am of course happy to answer any comments, ... | null | CC BY-SA 3.0 | null | 2010-11-03T15:47:20.410 | 2012-04-06T15:09:14.477 | 2012-04-06T15:09:14.477 | 264 | 264 | null |
4179 | 2 | null | 4174 | 2 | null | I think you could torture your data a bit with bootstrapping. Following cgillspies calculations with the geometric distribution, I played around a bit and came up with the following R-code - any corrections greatly appreciated:
```
fails <- c(100, 22, 36, 44, 89, 24, 74) # Observed data
N <- 100000 # Number of replicat... | null | CC BY-SA 2.5 | null | 2010-11-03T15:49:57.703 | 2010-11-03T19:18:57.650 | 2010-11-03T19:18:57.650 | 1766 | 1766 | null |
4180 | 2 | null | 4174 | 7 | null | This question asks for a [prediction limit](http://en.wikipedia.org/wiki/Prediction_interval). This tests whether a future statistic is "consistent" with previous data. (In this case, the future statistic is the post-fix value of 223.) It accounts for a chance mechanism or uncertainty in three ways:
- The data them... | null | CC BY-SA 2.5 | null | 2010-11-03T16:37:07.917 | 2010-11-04T04:13:33.710 | 2010-11-04T04:13:33.710 | 919 | 919 | null |
4181 | 2 | null | 4138 | 11 | null | The question reads to me like the OP was asking when $U = (X,Y,Z)^{\mathrm{T}}$ are jointly normal then what is the probability $P(X \geq Y \mbox{ and } X \geq Z)$?
For that question we could look at the joint distribution of $AU$ where $A$ looks like
$$
A=\left[
\begin{array}{ccc}
1 & -1 & 0 \newline
1 & 0 & -1
\end... | null | CC BY-SA 2.5 | null | 2010-11-03T16:51:13.893 | 2010-11-03T19:22:08.373 | 2010-11-03T19:22:08.373 | null | null | null |
4182 | 2 | null | 4174 | 1 | null | I faced this problem myself and decided to try Fisher's exact test. This has the advantage that the arithmetic boils down to something you can do with JavaScript. I put this on a [web page](https://web.archive.org/web/20120307183001/http://www.mcdowella.demon.co.uk/FlakyPrograms.html) - this should work either from the... | null | CC BY-SA 4.0 | null | 2010-11-03T18:48:02.257 | 2023-01-23T16:12:16.213 | 2023-01-23T16:12:16.213 | 362671 | 1789 | null |
4183 | 2 | null | 539 | 39 | null | I will assume that a "categorical" variable actually stands for an ordinal variable; otherwise it doesn't make much sense to treat it as a continuous one, unless it's a binary variable (coded 0/1) as pointed by @Rob. Then, I would say that the problem is not that much the way we treat the variable, although many models... | null | CC BY-SA 4.0 | null | 2010-11-03T20:14:59.663 | 2022-12-14T06:44:23.937 | 2022-12-14T06:44:23.937 | 362671 | 930 | null |
4184 | 1 | 4869 | null | 2 | 155 | [Cross post from [here](https://math.stackexchange.com/q/8830/2949), figured this community may be more relevant]
I am working in the field of machine learning, and I have come across a few papers that show relationships between Gröbner bases and discrete probability. So I come here for help.
Can you please explain how... | How can Gröbner bases used to describe discrete probability? | CC BY-SA 2.5 | null | 2010-11-04T00:01:32.990 | 2019-01-19T22:58:30.840 | 2019-01-19T22:58:30.840 | 99274 | 1793 | [
"probability",
"maple"
] |
4185 | 1 | 4186 | null | 2 | 4800 | [Here](http://uk.answers.yahoo.com/question/index?qid=20080702080708AAsRZpt) is a link that describes the formula to find the mode of grouped data.
[Here](http://www.tutors4you.com/modegraphically.htm) is a link that gives a graphical method to finding the mode of grouped data.
Question: Can someone please explain how ... | The formula for finding the mode of grouped data | CC BY-SA 2.5 | null | 2010-11-04T00:34:49.023 | 2010-11-04T15:30:42.843 | 2010-11-04T15:30:42.843 | 919 | 1636 | [
"descriptive-statistics"
] |
4186 | 2 | null | 4185 | 2 | null | I have not bothered to check the math at the link you gave but from the figure the mode is the intersection of the two diagonal lines. The end points of both the lines is known so all you need to do is to find out the intersection of those two lines to get the mode.
| null | CC BY-SA 2.5 | null | 2010-11-04T00:44:18.200 | 2010-11-04T00:44:18.200 | null | null | null | null |
4187 | 1 | null | null | 20 | 19235 | Hello
I have two problems that sound like natural candidates for multilevel/mixed models, which I have never used. The simpler, and one that I hope to try as an introduction, is as follows:
The data looks like many rows of the form
`x y innergroup outergroup`
where x is a numeric covariate upon which I want to regres... | Using lmer for prediction | CC BY-SA 2.5 | null | 2010-11-04T03:08:14.567 | 2022-05-15T12:13:42.687 | 2010-11-04T11:47:23.530 | 930 | 1777 | [
"r",
"mixed-model",
"maximum-likelihood",
"generalized-linear-model"
] |
4191 | 1 | 4194 | null | 17 | 8605 | MCMC algorithms like Metropolis-Hastings and Gibbs sampling are ways of sampling from the joint posterior distributions.
I think I understand and can implement metropolis-hasting pretty easily--you simply choose starting points somehow, and 'walk the parameter space' randomly, guided by the posterior density and prop... | Where do the full conditionals come from in Gibbs sampling? | CC BY-SA 3.0 | null | 2010-11-04T04:35:38.390 | 2013-11-24T16:47:46.673 | 2013-11-24T16:47:46.673 | 7290 | 1795 | [
"bayesian",
"markov-chain-montecarlo",
"gibbs"
] |
4193 | 1 | null | null | 14 | 911 | Most asymptotic results in statistics prove that as $n \rightarrow \infty$ an estimator (such as the MLE) converges to a normal distribution based on a second-order taylor expansion of the likelihood function. I believe there's a similar result in Bayesian literature, the "Bayesian Central Limit Theorem", which shows t... | Do third order asymptotics exist? | CC BY-SA 2.5 | null | 2010-11-04T05:49:02.013 | 2023-04-13T12:40:24.827 | 2017-06-06T01:01:41.487 | 11887 | 1760 | [
"mathematical-statistics",
"asymptotics",
"saddlepoint-approximation"
] |
4194 | 2 | null | 4191 | 7 | null | Yes, you are right, the conditional distribution needs to be found analytically, but I think there are lots of examples where the full conditional distribution is easy to find, and has a far simpler form than the joint distribution.
The intuition for this is as follows, in most "realistic" joint distributions $P(X_1,\... | null | CC BY-SA 2.5 | null | 2010-11-04T05:57:44.330 | 2010-11-04T05:57:44.330 | null | null | 1760 | null |
4196 | 1 | null | null | 1 | 341 | I have a time series $X(t)$. Each $X(t)$ has three possible outcomes: A, B or C. I am interested in the ratio of A, B and C to the total. Assuming $N$ is the number of data points I have gathered for $X(t)$,
How can I compute the confidence levels for A/N, B/N and C/N when the $X(t)$ are "intuitively" not independent ?... | Confidence interval for ratio in timeseries | CC BY-SA 4.0 | null | 2010-11-04T06:34:24.897 | 2022-12-22T14:45:34.203 | 2022-12-22T14:45:34.203 | 56940 | 1784 | [
"time-series",
"confidence-interval",
"non-independent"
] |
4197 | 2 | null | 4193 | 3 | null | Here is an attempt to answer your insightful question. I have seen the inclusion of the 3rd term of the Taylor series to increase the speed of convergence of the series to the true distribution. However, I haven't seen (in my limited experience) the usage of third and higher moments.
As pointed out by John D. Cook in ... | null | CC BY-SA 2.5 | null | 2010-11-04T06:37:44.763 | 2010-11-04T06:37:44.763 | null | null | 1307 | null |
4199 | 2 | null | 4193 | 3 | null | Definitely not my area, but I'm pretty sure third- and higher-order asymptotics exist. Is this any help?
Robert L. Strawderman. [Higher-Order Asymptotic Approximation: Laplace, Saddlepoint, and Related Methods](http://www.jstor.org/stable/2669788) Journal of the American Statistical Association Vol. 95, No. 452 (Dec., ... | null | CC BY-SA 2.5 | null | 2010-11-04T08:09:42.603 | 2010-11-04T08:09:42.603 | null | null | 449 | null |
4200 | 1 | null | null | 3 | 6834 | I have a large number (hundreds to thousands) of noisy time series that represent contemporaneous observations from different subjects.
I hypothesise that there exist lead-lag relationships between observations for different subjects (or groups of subjects.)
I would like to explore the potential use of such lead-lag re... | Using lead-lag relationships for time series prediction | CC BY-SA 2.5 | null | 2010-11-04T09:02:43.647 | 2017-04-22T23:58:07.200 | 2010-11-08T07:26:18.083 | 439 | 439 | [
"time-series"
] |
4201 | 2 | null | 2635 | 15 | null | I believe M. Tibbit's answer refers to the general case of a gamma with unknown shape and scale. If the shape α is known and the sampling distribution for x is gamma(α, β) and the prior distribution on β is gamma(α0, β0), the posterior distribution for β is gamma(α0 + nα, β0 + Σxi). See this [diagram](http://www.johndc... | null | CC BY-SA 3.0 | null | 2010-11-04T10:12:31.840 | 2016-05-28T13:27:54.763 | 2016-05-28T13:27:54.763 | 319 | 319 | null |
4202 | 2 | null | 4187 | 17 | null | Expressing factors relationships using R formulas follows from Wilkinson's notation, where '*' denotes crossing and '/' nesting, but there are some particularities in the way formula for mixed-effects models, or more generally random effects, are handled. For example, two crossed random effects might be represented as ... | null | CC BY-SA 3.0 | null | 2010-11-04T11:28:13.980 | 2017-09-15T23:46:59.690 | 2017-09-15T23:46:59.690 | 28564 | 930 | null |
4203 | 1 | 4206 | null | 1 | 1819 | Suppose I have a set of $N$ experimental points of the form
\begin{equation}
\{x_i, y_i, d_i\},
\end{equation}
where $i=1,...,N,$ and $d_i$ are errorbars for $y_i$. To fit the data, I minimize the reduced chi-square
\begin{equation}
\chi^2(p) = \sum_{i=1}^N \frac{[y_i - f(x_i,p)]^2}{d_i^2},
\end{equation}
where $f(x,p... | What is the distribution of a chi-square minimizing function? | CC BY-SA 2.5 | 0 | 2010-11-04T11:33:05.190 | 2010-11-04T13:28:31.173 | 2010-11-04T13:28:31.173 | 930 | 1197 | [
"distributions",
"chi-squared-test",
"fitting"
] |
4204 | 2 | null | 4187 | 10 | null | The [ez](http://cran.r-project.org/package=ez) package contains the ezPredict() function, which obtains predictions from lmer models where prediction is based on the fixed effects only. It's really just a wrapper around the approach detailed in the [glmm wiki](http://glmm.wikidot.com/faq).
| null | CC BY-SA 2.5 | null | 2010-11-04T12:36:04.560 | 2010-11-04T12:36:04.560 | null | null | 364 | null |
4205 | 2 | null | 4200 | 9 | null | You can choose from about 40 years of research and countless books, dissertations, monographs etc.
Given that your question is not all that focussed yet, maybe an introductory time-series book could help. In a nutshell, the autocorrelation function gives clues to lead/lag relationships that may be present in a singl... | null | CC BY-SA 2.5 | null | 2010-11-04T13:00:44.517 | 2010-11-04T13:00:44.517 | null | null | 334 | null |
4206 | 2 | null | 4203 | 1 | null | Another term for your fitting procedure would be weighted [non-linear least squares](http://en.wikipedia.org/wiki/Non-linear_least_squares). The weights are a very minor complication. Fitting non-linear least squares is more tricky than [ordinary least squares](http://en.wikipedia.org/wiki/Ordinary_least_squares), but ... | null | CC BY-SA 2.5 | null | 2010-11-04T13:13:23.710 | 2010-11-04T13:13:23.710 | null | null | 449 | null |
4209 | 2 | null | 4193 | 5 | null | It is not possible for a sequence to "converge" to one thing and then to another. The higher-order terms in an asymptotic expansion will go to zero. What they tell you is how close to zero they are for any given value of $n$.
For the Central Limit Theorem (as an example) the appropriate expansion is that of the logar... | null | CC BY-SA 4.0 | null | 2010-11-04T14:46:20.700 | 2022-09-21T17:53:16.627 | 2022-09-21T17:53:16.627 | 79696 | 919 | null |
4210 | 2 | null | 4193 | 7 | null | You are searching for the Edgeworth series aren't you?
See the Wikipedia article on [same](https://en.wikipedia.org/wiki/Edgeworth_series).
(note that Edgeworth died in 1926, should be in most famous statisticians? )
| null | CC BY-SA 4.0 | null | 2010-11-04T15:24:41.853 | 2023-04-13T12:40:24.827 | 2023-04-13T12:40:24.827 | 362671 | 223 | null |
4211 | 1 | 4213 | null | 18 | 3264 | I have a data like this:
```
> table(A,B,C)
, , C = FALSE
B
A FALSE TRUE
FALSE 177 42
TRUE 6 8
, , C = TRUE
B
A FALSE TRUE
FALSE 5 31
TRUE 4 10
```
How can I plot this on a single graph, possibly without imposing any hierarchy?
| How to visualize 3D contingency matrix? | CC BY-SA 2.5 | null | 2010-11-04T16:11:48.220 | 2012-01-10T19:17:41.530 | null | null | null | [
"data-visualization",
"contingency-tables"
] |
4212 | 2 | null | 2576 | 2 | null | You can rank ordinal distributions by means of an intuitive dominance criterion: the answers to one question are better than the answers to another when it is more likely than not that a randomly chosen answer to the first will be better than a randomly chosen answer to the second.
In more detail: put all the answers t... | null | CC BY-SA 2.5 | null | 2010-11-04T16:30:27.683 | 2010-11-04T20:10:51.363 | 2010-11-04T20:10:51.363 | 919 | 919 | null |
4213 | 2 | null | 4211 | 15 | null | I would try some kind of 3D heatmap, [mosaic plot](http://www.datavis.ca/papers/drew/) or a [sieve plot](http://www.improving-visualisation.org/visuals/tag=:sieve+plot/mode=1/sort=alpha) (available in the [vcd](http://cran.r-project.org/web/packages/vcd/index.html) package). Isn't the base `mosaicplot()` function worki... | null | CC BY-SA 2.5 | null | 2010-11-04T16:33:27.503 | 2010-11-04T17:34:35.440 | 2010-11-04T17:34:35.440 | 930 | 930 | null |
4214 | 1 | null | null | 7 | 388 | On both a practical and philosophical level, how should you choose the scope when performing multiple comparisons?
When a study performs 10 tests to check the hypothesis that 10 explanatory variable are predictive for "something" (on the same dataset), the test should obviously be corrected.
What if there where ten stu... | Choosing the scope when performing multiple comparisons? | CC BY-SA 2.5 | null | 2010-11-04T16:49:31.543 | 2010-11-04T19:10:54.393 | 2017-04-13T12:44:20.943 | -1 | 253 | [
"multiple-comparisons",
"meta-analysis"
] |
4215 | 2 | null | 2169 | 4 | null | As you know, from
$$Var[q] = Var[\sum_i w_i x_{(i)}] = \sum_i\sum_j w_i w_j Cov[x_{(i)}, x_{(j)}]$$
it follows you need only compute the variances and covariances of the order statistics. To do this, diagonalize the covariance matrix! Although this cannot be done in general, M. A. Stephens has [obtained (heuristicall... | null | CC BY-SA 2.5 | null | 2010-11-04T16:51:47.097 | 2010-11-04T16:51:47.097 | null | null | 919 | null |
4217 | 2 | null | 4214 | 2 | null | Think of the following two experiments:
Experiment A; Throw a fair coin 10 times to assess Prob(Heads).
Experiment B: Throw a fair dice 5 times to assess Prob(Face showing 1).
To take the coin toss [example](http://en.wikipedia.org/wiki/Multiple_comparisons#Example_.E2.80.94_Flipping_coins) from the wiki: We may wish t... | null | CC BY-SA 2.5 | null | 2010-11-04T18:14:15.557 | 2010-11-04T19:10:54.393 | 2010-11-04T19:10:54.393 | null | null | null |
4219 | 1 | 4227 | null | 7 | 3998 | I'm using McNemar's test. Basically this question is about best practices when reporting results using McNemar's test.
I want to report the effect size. What is a sensible effect size for McNemar's test? I've seen the odds ratio b/c and the proportions b/(b+c) and c/(b+c) both used in papers. If I say what b and c are ... | Effect size of McNemar's Test | CC BY-SA 2.5 | null | 2010-11-05T01:24:23.780 | 2010-11-05T10:40:01.677 | null | null | 1540 | [
"hypothesis-testing",
"nonparametric"
] |
4220 | 1 | 4223 | null | 181 | 117697 | On the [Wikipedia page about naive Bayes classifiers](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Testing), there is this line:
>
$p(\mathrm{height}|\mathrm{male}) = 1.5789$ (A probability distribution over 1 is OK. It is the area under the bell curve that is equal to 1.)
How can a value $>1$ be OK? I thou... | Can a probability distribution value exceeding 1 be OK? | CC BY-SA 3.0 | null | 2010-11-05T01:25:39.520 | 2021-07-07T12:27:34.507 | 2021-07-07T12:27:34.507 | 35989 | 226 | [
"probability",
"distributions",
"normal-distribution",
"density-function",
"faq"
] |
4221 | 2 | null | 4220 | 51 | null | This is a common mistake from not understanding the difference between probability mass functions, where the variable is discrete, and probability density functions, where the variable is continuous. See [What is a probability distribution](http://www.itl.nist.gov/div898/handbook/eda/section3/eda361.htm):
>
continuou... | null | CC BY-SA 2.5 | null | 2010-11-05T01:38:47.637 | 2010-11-05T15:20:19.447 | 2010-11-05T15:20:19.447 | 919 | 493 | null |
4222 | 2 | null | 4191 | 11 | null | I think you've missed the main advantage of algorithms like of Metropolis-Hastings. For Gibbs sampling, you will need to sample from the full conditionals. You are right, that is rarely easy to do. The main advantage of Metropolis-Hastings algorithms is that you can still sample one parameter at a time, but you only n... | null | CC BY-SA 2.5 | null | 2010-11-05T01:56:14.007 | 2010-11-05T02:03:58.703 | 2010-11-05T02:03:58.703 | 493 | 493 | null |
4223 | 2 | null | 4220 | 200 | null | That Wiki page is abusing language by referring to this number as a probability. You are correct that it is not. It is actually a probability per foot. Specifically, the value of 1.5789 (for a height of 6 feet) implies that the probability of a height between, say, 5.99 and 6.01 feet is close to the following unitle... | null | CC BY-SA 3.0 | null | 2010-11-05T02:32:49.170 | 2017-02-09T13:59:38.130 | 2017-02-09T13:59:38.130 | 919 | 919 | null |
4224 | 2 | null | 4062 | 5 | null | Did you see this post? [http://groups.google.com/group/ggplot2/browse_thread/thread/8e1efd0e7793c1bb](http://groups.google.com/group/ggplot2/browse_thread/thread/8e1efd0e7793c1bb)
Take the example, add coord_polar() and reverse the axes and you get pretty close:
```
library(cluster)
data(mtcars)
x <- as.phylo(hclust(d... | null | CC BY-SA 2.5 | null | 2010-11-05T03:17:06.993 | 2010-11-05T03:17:06.993 | null | null | 1809 | null |
4225 | 1 | 4249 | null | 3 | 470 | I was thinking about CI and subjective Bayesian and I have following two questions:
- If a subjective (not objective) Bayesian would care if her predictions don't do well in the real world.
- A classical statistician would not care if her confidence statement is (obviously) wrong for a given data set (as in Welch's P... | Subjective Bayesian's care for real world validation and classical statistician's worry about CI related paradoxes for a given data set? | CC BY-SA 2.5 | null | 2010-11-05T05:22:01.777 | 2010-11-05T18:03:22.370 | 2010-11-05T18:03:22.370 | 1307 | 1307 | [
"bayesian",
"confidence-interval"
] |
4226 | 1 | 4235 | null | 27 | 2204 | Quantum Mechanics has generalized probability theory to negative/imaginary numbers, mostly to explain interference patterns, wave/particle duality and generally weird things like that. It can be seen more abstractly, however, as a noncommutative generalisation of Bayesian probability (quote from Terrence Tao). I'm curi... | Do negative probabilities/probability amplitudes have applications outside quantum mechanics? | CC BY-SA 2.5 | null | 2010-11-05T06:35:47.567 | 2023-02-20T12:07:57.113 | 2010-11-05T06:45:53.887 | 1760 | 1760 | [
"probability"
] |
4227 | 2 | null | 4219 | 6 | null | In general, I think best practice for presenting measures of effect size depends on the question of interest and the usual practice in your field. There's little point reporting an effect measure that readers will be unfamiliar with.
Having said that, in this particular case if you want a single effect measure I think... | null | CC BY-SA 2.5 | null | 2010-11-05T07:17:15.547 | 2010-11-05T10:40:01.677 | 2010-11-05T10:40:01.677 | 449 | 449 | null |
4228 | 2 | null | 4093 | 0 | null | PCA results (the different dimensions or commponents) generally can't be translated into a real concept I think is wrong to assume that one of the components is "fear of bears" what lead you to think that was what the component meant?
Principal components procedure transforms your data matrix to a new data matrix with... | null | CC BY-SA 2.5 | null | 2010-11-05T07:42:26.210 | 2010-11-05T07:42:26.210 | null | null | 1808 | null |
4230 | 2 | null | 4089 | 0 | null | Maybe you are looking for the library ggplot2 that lets you plot things in a pretty way.
Or you can check this website that seems to have lots of R graphic utilities
[http://addictedtor.free.fr/graphiques/](http://addictedtor.free.fr/graphiques/)
| null | CC BY-SA 2.5 | null | 2010-11-05T07:58:21.050 | 2010-11-05T07:58:21.050 | null | null | 1808 | null |
4231 | 1 | null | null | 4 | 270 | I have heard the terms training and validating a model. I know that we select variables which are most statistically significant and we look for other things like multicollinearity. My question is:
what does traning a model involves more than this ?
| Training a model | CC BY-SA 2.5 | null | 2010-11-05T08:52:03.723 | 2010-11-05T12:50:14.080 | null | null | 1763 | [
"logistic"
] |
4233 | 1 | null | null | 3 | 642 | I know about $r^2$ tells you about the amount of variation that can be explained by the predictor variables. I have run a model in which the rsquare has value 0.3010 but has false positive rate of around 15.60%. So this model which is logit in nature predicts 84% cases right. I want to know two things:
1) Is this false... | Significance of $r^2$ value | CC BY-SA 2.5 | null | 2010-11-05T10:46:22.953 | 2010-11-06T16:17:06.830 | 2010-11-05T13:08:36.773 | 449 | 1763 | [
"hypothesis-testing",
"regression",
"logistic"
] |
4234 | 2 | null | 4093 | 5 | null | For me, PCA scores are just re-arrangements of the data in a form that allows me to explain the data set with less variables. The scores represent how much each item relates to the component. You can name them as per factor analysis, but its important to remember that they are not latent variables, as PCA analyses all ... | null | CC BY-SA 2.5 | null | 2010-11-05T11:19:36.060 | 2010-11-05T11:19:36.060 | null | null | 656 | null |
4235 | 2 | null | 4226 | 17 | null | Yes. I like the article Søren shared very much, and together with the references in that article I would recommend Muckenheim, W. et al. (1986). [A Review of Extended Probabilities](https://doi.org/10.1016/0370-1573(86)90110-9). Phys. Rep. 133 (6) 337-401. It's a physics paper for sure, but the applications there are... | null | CC BY-SA 4.0 | null | 2010-11-05T12:23:25.587 | 2023-02-20T11:41:03.587 | 2023-02-20T11:41:03.587 | 77222 | null | null |
4236 | 2 | null | 1980 | 3 | null | Looking for examples and practices is a good way to learn, but I just wanted to mention that reproducibility has not only technical/script rerun side, but also code style and structuring aspect, minimization of side effects in core functions etc.
I personally found that Chambers book Software for Data Analysis allows ... | null | CC BY-SA 2.5 | null | 2010-11-05T12:26:45.920 | 2010-11-05T12:26:45.920 | null | null | 1820 | null |
4237 | 2 | null | 4231 | 5 | null | Although the [curse of dimensionality](http://en.wikipedia.org/wiki/Curse_of_dimensionality) and multicollinearity are distinct issues, cross-validation is used for building a predictive model: we usually estimate parameters of our model on training samples, and assess its generalizability on test samples. This yields ... | null | CC BY-SA 2.5 | null | 2010-11-05T12:39:03.030 | 2010-11-05T12:50:14.080 | 2017-04-13T12:44:44.530 | -1 | 930 | null |
4238 | 1 | null | null | 5 | 187 | We are often interested in estimating the limiting distribution of a parameter in situations where the data exhibit dependence within clusters. For example, a study of the effects of a household-level treatment on household-level outcomes must contend with the possibility that households within villages will have corre... | Sources of within-cluster correlation other than "random shocks" | CC BY-SA 2.5 | null | 2010-11-05T13:32:21.913 | 2019-03-29T04:05:59.027 | 2017-11-12T17:23:18.740 | 11887 | 96 | [
"correlation",
"random-effects-model"
] |
4239 | 1 | null | null | 13 | 8644 | I am designing a questionnaire for my dissertation. I am in the process of validating the questionnaire I have applied a Cronbach's alpha test to the initial sample group. The responses to the questionnaire are on a Likert scale; can anyone suggest any further tests to apply to help test its validity. I am not an exp... | Validating questionnaires | CC BY-SA 2.5 | null | 2010-11-05T13:34:50.963 | 2016-09-26T10:27:54.403 | 2016-09-26T10:27:54.403 | 3277 | null | [
"survey",
"scales",
"psychometrics",
"scale-construction"
] |
4241 | 2 | null | 4165 | 0 | null | Other areas of application for stochastic processes: (1) Asymptotic theory: This builds on PeterR's comment about an IID sequence. Law of large numbers and central limit theorem results require an understanding of stochastic processes. This is so fundamental in so many areas of application that I am inclined to say th... | null | CC BY-SA 2.5 | null | 2010-11-05T14:04:18.887 | 2010-11-05T14:04:18.887 | null | null | 96 | null |
4242 | 2 | null | 4239 | 22 | null | I will assume that your questionnaire is to be considered as one unidimensional scale (otherwise, Cronbach's alpha doesn't make very much sense). It is worth running an exploratory factor analysis to check for that. It will also allow you to see how items relate to the scale (i.e., through their loadings).
Basic steps ... | null | CC BY-SA 2.5 | null | 2010-11-05T14:14:26.580 | 2010-11-30T12:37:24.433 | 2010-11-30T12:37:24.433 | 930 | 930 | null |
4243 | 2 | null | 4239 | 11 | null | While supporting everything said above, i would suggest that you do the following (in similiar enough order)
Firstly, you should be using R, if not you should start. The following advice is predicated on the use of R.
I'll assume that you have, at this point, calculated the descriptive statistics et al. If not, the psy... | null | CC BY-SA 2.5 | null | 2010-11-05T14:51:50.307 | 2010-11-05T14:51:50.307 | null | null | 656 | null |
4244 | 2 | null | 3616 | 2 | null | If you want to assume simple linear trend, you can take the difference of each data set at the various time points and test that the slope of the line is zero.
-Ralph Winters
| null | CC BY-SA 2.5 | null | 2010-11-05T14:54:01.720 | 2010-11-05T14:54:01.720 | null | null | null | null |
4245 | 1 | 4246 | null | 10 | 24189 | My problem with understanding this expression might come from the fact that English is not my first language, but I don't understand why it's used in this way.
The marginal mean is typically the mean of a group or subgroup's measures of a variable in an experiment, but why not just use the word mean? What's the margina... | What is the meaning of 'Marginal mean'? | CC BY-SA 2.5 | null | 2010-11-05T14:54:16.593 | 2016-08-31T05:24:35.263 | 2010-11-05T15:04:38.387 | null | 1320 | [
"terminology",
"marginal-distribution"
] |
4246 | 2 | null | 4245 | 7 | null | Perhaps, the term originates from how the data is represented in a contingency table. See this [example](http://en.wikipedia.org/wiki/Contingency_table#Example) from the wiki.
In the above example, we would speak of marginal totals for gender and handedness when referring to the last column and the bottom row respectiv... | null | CC BY-SA 2.5 | null | 2010-11-05T15:01:44.253 | 2010-11-05T15:01:44.253 | null | null | null | null |
4248 | 2 | null | 4226 | 18 | null | QM does not use negative or imaginary probabilities: if it did, they would no longer be probabilities!
What can be (and usually is) a complex value is the quantum mechanical wave function $\psi$. From it the probability amplitude (which is a bona fide probability density) can be constructed; it is variously written $\... | null | CC BY-SA 4.0 | null | 2010-11-05T15:30:54.033 | 2023-02-20T12:07:57.113 | 2023-02-20T12:07:57.113 | 362671 | 919 | null |
4249 | 2 | null | 4225 | 3 | null | For many reasons you're right about 1. I certainly wouldn't heed the advice of someone who did not care about whether it is any good!
Number 2, as you have expressed it, does not characterize good practice. If there are possible datasets where a CI (or any decision procedure, for that matter) is clearly wrong, then t... | null | CC BY-SA 2.5 | null | 2010-11-05T16:02:06.010 | 2010-11-05T16:02:06.010 | null | null | 919 | null |
4250 | 2 | null | 4245 | 4 | null | I'd assume it means the sample analogue of the marginal expectation $\operatorname{E}(X)$, as opposed to the sample analogue of a conditional expectation $\operatorname{E}(X \mid Y)$, where $Y$ could be anything.
| null | CC BY-SA 2.5 | null | 2010-11-05T16:42:02.713 | 2010-11-05T17:00:14.750 | 2010-11-05T17:00:14.750 | 449 | 449 | null |
4251 | 2 | null | 4225 | 4 | null | For the second question, I believe the answer is "Yes". I will quote Andrew Gelman here, "..in general there is no coverage guarantee because frequency properties depend on nuisance
parameters which can only be ignored in some special cases of pivotal test statistics".
You can take a look at the following paper for so... | null | CC BY-SA 2.5 | null | 2010-11-05T17:33:07.933 | 2010-11-05T17:33:07.933 | null | null | 1831 | null |
4252 | 1 | 4254 | null | 10 | 88147 | How can I calculate the truncated or trimmed mean? Let's say truncated by 10%?
I can imagine how to do it if you have 10 entries or so, but how can I do it for a lot of entries?
| How to calculate the truncated or trimmed mean? | CC BY-SA 3.0 | null | 2010-11-05T17:35:33.833 | 2016-02-17T11:02:00.770 | 2013-03-03T16:05:18.663 | 603 | 1833 | [
"mean",
"robust",
"truncation",
"trimmed-mean"
] |
4253 | 2 | null | 4089 | 0 | null | Its probably not exactly what you are looking for, but the pairs.panels() function in the psych package for R may prove useful. It gives you correlation values in the upper diagonal, loess lines and points in the lower diagonal, and shows a histogram of each variable's scores in the diagonal line of the matrix. I pers... | null | CC BY-SA 2.5 | null | 2010-11-05T17:44:49.703 | 2010-11-05T17:44:49.703 | null | null | 656 | null |
4254 | 2 | null | 4252 | 21 | null | Trimmed mean involves trimming $P$ percent observations from both ends.
E.g.: If you are asked to compute a 10% trimmed mean, $P = 10$.
Given a bunch of observations, $X_i$:
- First find $n$ = number of observations.
- Reorder them as "order statistics" $X_i$ from the smallest to the largest.
- Find lower case $p ... | null | CC BY-SA 3.0 | null | 2010-11-05T17:52:59.327 | 2013-06-26T06:06:11.103 | 2013-06-26T06:06:11.103 | 805 | 69 | null |
4255 | 2 | null | 4089 | 1 | null | To explore dataset I really like `rattle`. Install the package and just call `rattle()`. The interface is quite self explainatory.
| null | CC BY-SA 2.5 | null | 2010-11-05T18:42:31.133 | 2010-11-05T18:42:31.133 | null | null | 582 | null |
4256 | 2 | null | 2397 | 1 | null | Is it not the case that $|S_{-i}|=|S_{-j}|$ for all $i,j$ where $S_{-i}$ is the Multinomial covariance matrix with the $i$-th row and column removed? Since this is the case, I don't understand what you mean by "freedom of choice" as any "choice" is equivalent.
| null | CC BY-SA 2.5 | null | 2010-11-05T19:28:01.477 | 2010-11-05T19:33:51.173 | 2010-11-05T19:33:51.173 | 1835 | 1835 | null |
4257 | 2 | null | 4233 | 4 | null | First, no model is perfect unless it is over-fit. So, your false positive rate is not unusual. Is a false positive rate of 16% good or bad? If it is lower than the natural proportion in the data it is OK. If it is not it is really bad. The key is by how much your model reduces the error rate. That's measured in s... | null | CC BY-SA 2.5 | null | 2010-11-05T20:41:18.610 | 2010-11-06T16:17:06.830 | 2010-11-06T16:17:06.830 | 1329 | 1329 | null |
4258 | 1 | 4317 | null | 14 | 2393 | I would like to automate the choice of burn-in for an MCMC chain, e.g. by removing the first n rows based on a convergence diagnostic.
To what extent can this step be safely automated? Even if I still double check the autocorrelation, mcmc trace, and pdfs, it would be nice to have the choice of burn-in length automate... | Can I semi-automate MCMC convergence diagnostics to set the burn-in length? | CC BY-SA 2.5 | null | 2010-11-05T21:17:42.250 | 2010-11-10T00:17:12.523 | 2010-11-09T16:23:28.803 | 1381 | 1381 | [
"r",
"bayesian",
"markov-chain-montecarlo"
] |
4259 | 1 | null | null | 10 | 1585 | I work in the field of data mining and have had very little formal schooling in statistics. Lately I have been reading a lot of work that focuses on Bayesian paradigms for learning and mining, which I find very interesting.
My question is (in several parts), given a problem is there a general framework by which it is... | Tips and tricks to get started with statistical modeling? | CC BY-SA 2.5 | null | 2010-11-05T21:17:57.507 | 2022-12-03T04:30:54.830 | 2010-11-06T16:43:06.420 | null | null | [
"bayesian",
"modeling",
"references",
"exploratory-data-analysis"
] |
4260 | 2 | null | 2343 | 1 | null | Let $\tilde{\omega}$ be the equivalence class of a given tree $\omega$ (i.e. $\omega_1 \sim \omega_2$ iif $\omega_1 \in \tilde{\omega}_2$). In your question, you define a probability (say $\tilde{P}$) on the equivalence class and you want a formula to compute rapidly $\tilde{P}(\tilde{\omega}=\tilde{w})$.
Obviously th... | null | CC BY-SA 2.5 | null | 2010-11-05T21:33:34.133 | 2010-11-22T16:36:15.373 | 2010-11-22T16:36:15.373 | 223 | 223 | null |
4261 | 1 | 4263 | null | 3 | 1144 | This relates to a previous question of mine which didn't gain many responses, perhaps because it wasn't very clear nd well written. I hope this time I will be more accurate and get your much appreciated assistance.
I am analyzing results of a biological experiment. The results given as a single value ( non-negative int... | Should I use an average ECDF? | CC BY-SA 2.5 | null | 2010-11-05T22:12:23.317 | 2023-03-03T13:35:06.313 | null | null | 634 | [
"r",
"statistical-significance",
"sampling",
"permutation-test"
] |
4262 | 1 | null | null | 3 | 1873 | I have a process which writes statistics from a server system to a file each second in this format:
```
label1 label2 label3
344 666 787
344 849 344
939 994 344
```
There are a number of different values which I need graphs for, and each value is added to the bottom of the file each second.
I am looking for a nice wa... | Graphing real-time data from a text file | CC BY-SA 2.5 | null | 2010-11-05T22:50:38.007 | 2010-11-06T00:36:39.673 | null | null | 1845 | [
"data-visualization",
"real-time"
] |
4263 | 2 | null | 4261 | 1 | null | To average the ECDFs, I'd do something like:
```
impute_resolution = 1e3
values_to_impute = seq(
min(my_data$true_data)
, max(my_data$true_data)
, length.out = impute_resoluton
)
ecdfs = matrix(NA,nrow=length(values_to_impute))
for(i in 1:(ncol(my_data)-1)){ #assumes column 1 is true_data
this_ecdf = ... | null | CC BY-SA 2.5 | null | 2010-11-05T23:20:38.963 | 2010-11-05T23:20:38.963 | null | null | 364 | null |
4264 | 2 | null | 4262 | 7 | null | I had really good luck with the KDE program [kst](http://kst.kde.org):
>
Kst is the fastest real-time large-dataset viewing and plotting tool available
and has basic data analysis functionality.
Kst contains many powerful built-in features and is expandable with plugins
and extensions. Extensive help is availabl... | null | CC BY-SA 2.5 | null | 2010-11-05T23:43:15.160 | 2010-11-05T23:43:15.160 | null | null | 334 | null |
4265 | 2 | null | 4262 | 2 | null | RRDTool looks like it might be exactly what you are looking for. I've never tried to run it on a Mac but it looks like someone has some info on that here: [http://rrdtool.darwinports.com/](http://rrdtool.darwinports.com/)
Good luck!
| null | CC BY-SA 2.5 | null | 2010-11-06T00:36:39.673 | 2010-11-06T00:36:39.673 | null | null | 118 | null |
4266 | 2 | null | 2397 | 2 | null | There no inherent problem with the singular covariance here. Your asymptotic distribution is the singular normal. See [http://fedc.wiwi.hu-berlin.de/xplore/tutorials/mvahtmlnode34.html](http://fedc.wiwi.hu-berlin.de/xplore/tutorials/mvahtmlnode34.html) which gives the density of the singular normal.
| null | CC BY-SA 2.5 | null | 2010-11-06T00:52:44.303 | 2010-11-06T00:52:44.303 | null | null | 1860 | null |
4267 | 1 | 4268 | null | 18 | 1771 | I recently read Skillicorn's book on matrix decompositions, and was a bit disappointed, as it was targeted to an undergraduate audience. I would like to compile (for myself and others) a short bibliography of essential papers (surveys, but also breakthrough papers) on matrix decompositions. What I have in mind primaril... | Essential papers on matrix decompositions | CC BY-SA 2.5 | null | 2010-11-06T03:32:17.627 | 2010-12-16T08:38:52.443 | 2010-11-06T18:59:40.517 | null | 30 | [
"matrix-decomposition",
"svd",
"numerics"
] |
4268 | 2 | null | 4267 | 17 | null | How do you know that SVD and NMF are by far the most used [matrix decompositions](http://en.wikipedia.org/wiki/Matrix_decomposition) rather than LU, Cholesky and QR? My personal favourite 'breakthrough' would have to be the guaranteed rank-revealing QR algorithm,
- Chan, Tony F. "Rank revealing QR factorizations". Lin... | null | CC BY-SA 2.5 | null | 2010-11-06T07:17:02.640 | 2010-11-06T08:39:48.787 | 2010-11-06T08:39:48.787 | 449 | 449 | null |
4269 | 2 | null | 4175 | 1 | null | From Sidney Siegel:
>
With a large enough sample the binomial distribution tends toward the normal distribution. A rule of thumb is that NPQ must be equal to at least 9.
I believe in this case it is 750 independent observations * 1/5 *4/5 =120. Thus the parametric one-sample t-test is appropriate and the most powerf... | null | CC BY-SA 2.5 | null | 2010-11-06T10:28:50.867 | 2010-11-06T10:28:50.867 | null | null | 1614 | null |
4270 | 2 | null | 4044 | 10 | null | The most complete survey is provided in [Statistical Inference Based on Divergence Measures](http://books.google.com/books?id=ziDGGIkhqlMC&pg=PA493&dq=Leandro+Pardo+Complutense+University,+Chapman+Hall+2006&hl=en&ei=Zm_VTOqGI8H6lwepsfn9CA&sa=X&oi=book_result&ct=result&resnum=1&ved=0CC4Q6AEwAA#v=onepage&q&f=false) by Le... | null | CC BY-SA 2.5 | null | 2010-11-06T11:07:48.890 | 2010-11-06T15:10:28.317 | 2010-11-06T15:10:28.317 | 919 | 1873 | null |
4271 | 2 | null | 1645 | 2 | null | In fact the Kiefer Salmon test and the Jarque Bera test are critically different as shown in several places but most recently [here](http://www.econ.sinica.edu.tw/upload/file/1118.pdf) -Moment Tests for Standardized Error Distributions: A Simple Robust Approach by Yi-Ting Chen. The Kiefer Salmon test by construction i... | null | CC BY-SA 2.5 | null | 2010-11-06T14:06:09.300 | 2010-11-06T14:06:09.300 | null | null | 1873 | null |
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