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9924 | 2 | null | 9917 | 3 | null | Rather than "Fix a wall at time $T$ and select the best candidate after $T$", I think you may be using "Fix a wall at time $T$ and select the first candidate to arrive at or after $T$ who is better than all those arriving before time $T$". This is a continuous version of the [secretary problem](http://en.wikipedia.org... | null | CC BY-SA 3.0 | null | 2011-04-24T13:40:16.647 | 2011-04-24T13:40:16.647 | null | null | 2958 | null |
9925 | 2 | null | 9913 | 6 | null | whuber has made the point in the comment.
If $\log_e(Y) = B_0 + B_1\log_e(X) + U$ and $U$ is independent of $X$ then taking the partial derivative with respect to $X$ gives $\frac{\partial Y}{\partial X}\cdot\frac{1}{Y} = B_1\frac{1}{X}$, i.e. $B_1 = \frac{\partial Y}{\partial X}\cdot\frac{X}{Y}$.
$E_{y,x} = \lim_{X ... | null | CC BY-SA 3.0 | null | 2011-04-24T14:05:19.113 | 2011-04-24T14:05:19.113 | null | null | 2958 | null |
9926 | 1 | 9975 | null | 13 | 534 | Is there any use for the quantity
$$
\int f(x)^2 dx
$$
in statistics or information theory?
| Is there any use for the quantity $\int f(x)^2 dx$ in statistics or information theory? | CC BY-SA 3.0 | null | 2011-04-24T15:05:10.177 | 2011-04-29T03:05:15.267 | 2011-04-29T03:05:15.267 | 2970 | 3567 | [
"probability",
"entropy",
"information-theory"
] |
9927 | 2 | null | 9918 | 7 | null | The way I read your terminology, what you want is first to assess internal consistency within each group of variables, and then to assess the correlations among the scale scores which constitute the average of each group of variables. The first can be done using Cronbach's alpha, and the second using Pearson correlati... | null | CC BY-SA 3.0 | null | 2011-04-24T15:48:08.573 | 2013-01-22T18:52:18.873 | 2013-01-22T18:52:18.873 | 2669 | 2669 | null |
9928 | 1 | 9936 | null | 3 | 602 | What "fat-tailed distributions" $p(x)$, symmetric about zero, have the property
$$\newcommand{\e}{\mathbb{E}}\newcommand{\rd}{\mathrm{d}}
\e e^X = \int_{-\infty}^{\infty} e^x p(x) \rd x < \infty \> ?
$$
Context
I'm attempting to price financial options for $X$ without using the Black–Scholes formula. It is usually ea... | Symmetric fat-tailed distributions where $\mathbb{E} e^X < \infty$ | CC BY-SA 3.0 | null | 2011-04-24T18:07:51.337 | 2011-04-27T16:44:25.453 | 2011-04-27T16:44:25.453 | null | null | [
"distributions"
] |
9930 | 1 | null | null | 18 | 7915 | This is probably demonstrating a fundamental lack of understanding of how partial correlations work.
I have 3 variables, x,y,z. When I control for z, the correlation between x and y increases over the correlation between x and y when z was not controlled for.
Does this make sense? I tend to think that when one controls... | Does it make sense for a partial correlation to be larger than a zero-order correlation? | CC BY-SA 3.0 | null | 2011-04-24T20:08:52.457 | 2021-12-10T11:29:35.833 | 2011-04-29T00:49:44.813 | 3911 | 4307 | [
"correlation"
] |
9931 | 1 | 9955 | null | 6 | 16779 | I have a problem where I need to calculate linear regression as samples come in. Is there a formula that I can use to get the exponentially weighted moving linear regression? Not sure if that's what you would call it though.
| Exponentially weighted moving linear regression | CC BY-SA 3.0 | null | 2011-04-24T18:56:26.757 | 2021-08-21T08:26:22.517 | 2011-04-24T20:24:05.063 | 4306 | 4306 | [
"regression"
] |
9932 | 2 | null | 9930 | 2 | null | I think you need to know about moderator and mediator variables. The classic paper is Baron and Kenny [cited 21,659 times]
A moderator variable
>
"In general terms, a moderator is a
qualitative (e.g., sex, race, class)
or quantitative (e.g., level of
reward) variable that affects the
direction and/or strength... | null | CC BY-SA 3.0 | null | 2011-04-24T20:53:18.513 | 2011-04-25T05:19:38.197 | 2011-04-25T05:19:38.197 | 183 | 3597 | null |
9933 | 2 | null | 9931 | 7 | null | Sure, just add a `weights=` argument to `lm()` (in case of [R](http://www.r-project.org)):
```
R> x <- 1:10 ## mean of this is 5.5
R> lm(x ~ 1) ## regression on constant computes mean
Call:
lm(formula = x ~ 1)
Coefficients:
(Intercept)
5.5
R> lm(x ~ 1, weights=0.9^(seq(10,1,by=-1)))
Call:
lm(formu... | null | CC BY-SA 3.0 | null | 2011-04-24T21:27:08.933 | 2011-04-24T21:27:08.933 | null | null | 334 | null |
9934 | 2 | null | 9918 | 2 | null | I would suggest using as a replacement for the notion of correlation, which is defined only for pair-wise, the notion of mutual information and integration in Gaussian models.
In Gaussian models, integration of a group of variables $G_1$ is defined as the entropy of the group:
$I_1 \propto log(|C_1|)$
where $C_1$ is th... | null | CC BY-SA 3.0 | null | 2011-04-24T22:34:58.023 | 2011-04-24T22:34:58.023 | null | null | 1265 | null |
9935 | 2 | null | 9895 | 1 | null | I think this is is not a good question: first having $N_e$ on both sides of the model, and second I suspect it is ill conditioned.
Here are my explorations. Let's use R to try to minimise the sum of squares of the difference between the right hand side and $N_e$ by setting up two functions with `param[1]=b`, `param[2]... | null | CC BY-SA 3.0 | null | 2011-04-25T00:02:07.613 | 2011-04-25T00:02:07.613 | null | null | 2958 | null |
9936 | 2 | null | 9928 | 1 | null | The variance gamma process is a useful way to go. It is an extension of the standard brownian motion process, $Z(t)$
$$Y(t;\mu,\sigma,\nu)=\mu \Gamma(t;1,\nu) + \sigma Z(\Gamma[t;1,\nu])$$
Where $\Gamma(t;1,\nu)$ is a gamma process, with independent gamma distributed increments. So $\Gamma(t+s;1,\nu)-\Gamma(t;1,\nu)$... | null | CC BY-SA 3.0 | null | 2011-04-25T00:24:40.020 | 2011-04-26T12:14:57.870 | 2011-04-26T12:14:57.870 | 2392 | 2392 | null |
9937 | 1 | 9938 | null | 11 | 43465 | In a data frame, I would like to get the column's index by name. For example:
```
x <- data.frame(foo=c('a','b','c'),bar=c(4,5,6),quux=c(4,5,6))
```
I want to know the column index for "bar".
I came up with the following but it seems inelegant. Is there a more straightforward builtin that I am missing?
```
seq(1,len... | Finding the column index by its name in R | CC BY-SA 3.0 | null | 2011-04-25T00:44:09.760 | 2011-04-25T14:45:57.530 | 2011-04-25T14:45:57.530 | null | 1138 | [
"r"
] |
9938 | 2 | null | 9937 | 22 | null | probably this is the simplest way:
```
which(names(x)=="bar")
```
| null | CC BY-SA 3.0 | null | 2011-04-25T00:59:02.707 | 2011-04-25T00:59:02.707 | null | null | 2280 | null |
9939 | 2 | null | 9930 | 7 | null | Looking at the wikipedia page we have the partial correlation between $X$ and $Y$ given $Z$ is given by:
$$\rho_{XY|Z}=\frac{\rho_{XY}-\rho_{XZ}\rho_{YZ}}{\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}}>\rho_{XY}$$
So we simply require
$$\rho_{XY}>\frac{\rho_{XZ}\rho_{YZ}}{1-\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}}$... | null | CC BY-SA 3.0 | null | 2011-04-25T01:01:49.443 | 2011-04-29T01:13:22.797 | 2011-04-29T01:13:22.797 | 3911 | 2392 | null |
9940 | 1 | null | null | 2 | 142 | I have $n$ standard normal and independent random variables $X_i$ (In reality I have a large known number of them, but let's just say I have $n$). In my experiment I want to on average get exactly 3 random variables $X_i$ under a threshold $c$. To get that, I can compute $c$ having that property easily, because the ave... | Few random variables cannot influence $n$ independent others too much? | CC BY-SA 3.0 | null | 2011-04-25T05:40:10.820 | 2011-04-25T20:32:48.797 | 2011-04-25T18:23:15.007 | 4312 | 4312 | [
"probability",
"normal-distribution",
"random-variable"
] |
9941 | 2 | null | 9918 | 5 | null |
- The standard tools, at least in psychology, in your situation would be exploratory and confirmatory factor analysis to assess the convergence of the inter-item correlation matrix with some proposed model of the relationship between factors and items. The way that you have phrased your question suggests that you migh... | null | CC BY-SA 3.0 | null | 2011-04-25T05:43:44.557 | 2011-04-25T05:43:44.557 | null | null | 183 | null |
9942 | 1 | 9943 | null | 4 | 455 | In the one-dimensional case, if $X$ is $\mathcal{N}(\mu,\sigma^2)$, then $Y =\alpha X + \beta $ is $\mathcal{N}(\alpha \mu + \beta,\alpha^2\sigma^2)$ . We can prove this using the cumulative distribution function of of $Y$
$F_Y(a) = P\{Y \leq a\} = P\{\alpha X + \beta \leq a\} = P\{X \leq (a-\beta)/\alpha\}$.
Substitut... | Does $Y=\alpha X + \beta$ hold for multivariate gaussian density? | CC BY-SA 3.0 | null | 2011-04-25T06:08:43.413 | 2011-04-29T00:54:00.753 | 2011-04-29T00:54:00.753 | 3911 | 4290 | [
"multivariate-analysis"
] |
9943 | 2 | null | 9942 | 7 | null | The method of characteristic functions (CF) will work here. So we have the CF for $X$ as
$$\varphi_{X}(t)=\exp\left(it^{T}\mu_{X}-\frac{1}{2}t^{T}\Sigma_{X}t\right)$$
Now we make the substitution $Y=\alpha X + \beta$ in the CF and we get:
$$\varphi_{Y}(t)=E\left[\exp(it^{T}Y)\right]=E\left[\exp(it^{T}\alpha X +it^{T}... | null | CC BY-SA 3.0 | null | 2011-04-25T07:00:19.810 | 2011-04-26T11:59:13.590 | 2011-04-26T11:59:13.590 | 2970 | 2392 | null |
9944 | 2 | null | 9918 | 16 | null | What @rolando suggested looks like a good start, if not the whole response (IMO). Let me continue with the correlational approach, following the Classical Test Theory (CTT) framework. Here, as noted by @Jeromy, a summary measure for your group of characteristics might be considered as the totalled (or sum) score of all... | null | CC BY-SA 3.0 | null | 2011-04-25T08:25:35.240 | 2011-04-25T08:25:35.240 | null | null | 930 | null |
9946 | 2 | null | 9911 | 1 | null | This smells like archetypal analysis -- extracting some underlying prototypical objects. However, the vanilla AA will give you linear combination as PCA; thus I would suggest making something similar by first making some k-means-like clustering of the events and then selecting those which are closest to the centroids. ... | null | CC BY-SA 3.0 | null | 2011-04-25T08:54:17.140 | 2011-04-25T08:54:17.140 | null | null | null | null |
9947 | 1 | 9963 | null | 7 | 702 | For a Dataset $D$, we have gold standard centroids say $c_1, c_2, \cdots, c_n$. Now if we run k-means algorithm on $D$ with input $n$, we get k-means centroid $k_1, k_2, \cdots, k_n$.
I just wanted to know, is there any algorithm/heuristic to match the centroids between $k_i$ and $c_j$ where $i, j= 1, \cdots, n$ (One t... | Centroid matching problem | CC BY-SA 3.0 | null | 2011-04-25T09:04:15.967 | 2011-04-26T04:29:33.870 | 2011-04-25T09:22:45.317 | null | 4290 | [
"clustering",
"algorithms"
] |
9948 | 1 | null | null | 4 | 561 | I need to generate cross variograms of images using moving windows. For that I use the following equation:
$$
\gamma_{jk}(h)=\frac{1}{2n(h)}\sum_{i=1}^{n(h)}\Big\{\big[dn_j(x_i)-dn_j(x_i+h)\big]\cdot\big[dn_k(x_i)-dn_k(x_i+h)\big]\Big\}
$$
The first part stands for one band(j) and next part of band k. To illustrate ... | Cross variogram with a moving window | CC BY-SA 3.0 | null | 2011-04-25T09:12:07.153 | 2011-04-25T18:03:19.457 | 2011-04-25T17:13:52.510 | 4313 | 4313 | [
"variance",
"matlab",
"image-processing"
] |
9949 | 2 | null | 9937 | 12 | null | just to add another possibility:
You can usually use `grep` and it's decedents (i.e., grepl, to do these kind of jobs in a more sophisiticated way using regular expressions.
On your example your could get the column index with:
`grep("^bar$", colnames(x))` or `grep("^bar$", names(x))`
The `^` and `$` are meta characte... | null | CC BY-SA 3.0 | null | 2011-04-25T10:02:14.867 | 2011-04-25T10:02:14.867 | 2017-05-23T12:39:26.203 | -1 | 442 | null |
9950 | 2 | null | 9931 | 1 | null | If you form the Transfer Function Model y(t)=W(B)*X(t)+[THETA(B)/PHI(B)]*a(t) the operator [THETA(B)/PHI(B)] is the "smoothing component". For examnple if PHI(B)=1.0 and THETA(B)=1-.5B this would imply a set of weights of .5,.25,.125,... . in this way you could provide the answer to optimizing the "weighted moving line... | null | CC BY-SA 3.0 | null | 2011-04-25T10:49:36.277 | 2011-04-25T10:49:36.277 | null | null | 3382 | null |
9951 | 1 | null | null | 17 | 34590 | I was wrestling with stationarity in my head for a while... Is this how you think about it? Any comments or further thoughts will be appreciated.
>
Stationary process is the one which
generates time-series values such that
distribution mean and variance is kept
constant. Strictly speaking, this is
known as wea... | Intuitive explanation of stationarity | CC BY-SA 3.0 | null | 2011-04-25T12:18:37.257 | 2019-01-16T15:35:36.007 | 2019-01-16T15:35:36.007 | 11887 | 333 | [
"time-series",
"stationarity",
"intuition"
] |
9952 | 2 | null | 9947 | 0 | null | Sounds like you might want to consider using/writing an energy function. More here: [http://en.wikipedia.org/wiki/Optimization_%28mathematics%29#Multi-objective_optimization](http://en.wikipedia.org/wiki/Optimization_%28mathematics%29#Multi-objective_optimization)
I suppose if your number of k centroids is "small" yo... | null | CC BY-SA 3.0 | null | 2011-04-25T12:43:32.667 | 2011-04-25T12:43:32.667 | null | null | 4316 | null |
9953 | 2 | null | 9852 | 9 | null | As whuber stated this actually is a case of nested models, and hence one can apply a [likelihood-ratio test](http://en.wikipedia.org/wiki/Likelihood-ratio_test). Because it is still not exactly clear what models you are specifying I will just rewrite them in this example;
So model 1 can be:
$Y = a_1 + B_{11}(X) + B_{12... | null | CC BY-SA 3.0 | null | 2011-04-25T12:57:28.647 | 2011-04-25T12:57:28.647 | 2017-04-13T12:44:51.060 | -1 | 1036 | null |
9954 | 2 | null | 9948 | 4 | null | I prefer a slight change of notation due to the many $n$'s appearing in the original. Let $\alpha$ and $\beta$ designate the images. Let $i$ and $j$ each designate pairs of indexes into the image rows and columns. (Indexing goes from $1$ to $m$ for rows and $1$ to $n$ for columns.) Let $h$ designate a relative index... | null | CC BY-SA 3.0 | null | 2011-04-25T14:50:39.467 | 2011-04-25T18:03:19.457 | 2011-04-25T18:03:19.457 | 919 | 919 | null |
9955 | 2 | null | 9931 | 6 | null | Sounds like what you want to do is a two-stage model. First transform your data into exponentially smoothed form using a specified smoothing factor, and then input the transformed data into your linear regression formula.
[http://www.jstor.org/pss/2627674](http://www.jstor.org/pss/2627674)
[http://en.wikipedia.org/wi... | null | CC BY-SA 3.0 | null | 2011-04-25T15:18:03.097 | 2011-04-25T15:18:03.097 | null | null | 3489 | null |
9956 | 2 | null | 8696 | 6 | null | Try
```
computeFunction=function(onWhat,what,...){foreach(i=onWhat) %do% what(i,...)},
```
| null | CC BY-SA 3.0 | null | 2011-04-25T15:34:00.080 | 2011-04-25T15:34:00.080 | null | null | null | null |
9957 | 2 | null | 9931 | 4 | null | If you are looking for an equation of the form
$$y=\alpha_n + \beta_n x$$
after $n$ pieces of data have come in, and you are using an exponential factor $k \ge 1$ then you could use
$$\beta_n = \frac{\left(\sum_{i=1}^n k^i\right) \left(\sum_{i=1}^n k^i X_i Y_i\right) - \left(\sum_{i=1}^n k^i X_i\right) \left(\sum_... | null | CC BY-SA 3.0 | null | 2011-04-25T16:20:15.547 | 2011-04-25T16:20:15.547 | null | null | 2958 | null |
9958 | 2 | null | 9880 | 4 | null | For models of speeded decision tasks, check out the diffusion model and the linear ballistic accumulator; Donkin et al (2011, [pdf](http://mypage.iu.edu/~cdonkin/pubs/pbr11.pdf)) provide a good overview of these models and their different behaviours. There is R code out there for both these models. You might also do a ... | null | CC BY-SA 3.0 | null | 2011-04-25T16:46:01.893 | 2011-04-25T16:55:53.633 | 2011-04-25T16:55:53.633 | 364 | 364 | null |
9959 | 1 | null | null | 8 | 3718 | In [An Empirical Comparison of Supervised Learning Algorithms](http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf) (ICML 2006) the authors (Rich Caruana and Alexandru Niculescu-Mizil) evaluated several classification algorithms (SVMs, ANN, KNN, Random Forests, Decision Trees, etc.), and reported that c... | Calibrated boosted decision trees in R or MATLAB | CC BY-SA 3.0 | null | 2011-04-25T16:46:53.890 | 2011-04-26T13:01:49.497 | 2011-04-26T13:01:49.497 | 2798 | 2798 | [
"r",
"classification",
"matlab"
] |
9960 | 2 | null | 9959 | 3 | null | About R, I would vote for the [gbm](http://cran.r-project.org/web/packages/gbm/index.html) package; there's a vignette that provides a good overview: [Generalized Boosted Models: A guide to the gbm package](http://cran.r-project.org/web/packages/gbm/vignettes/gbm.pdf). If you are looking for an unified interface to ML ... | null | CC BY-SA 3.0 | null | 2011-04-25T17:06:02.400 | 2011-04-25T19:01:26.750 | 2011-04-25T19:01:26.750 | 930 | 930 | null |
9961 | 1 | 9973 | null | 8 | 3312 | I am new to evolutionary algorithm. I have studied Covariance Matrix Adaptation Evolution Strategy. I am not good at statistics. So could you please explain me in simple language (I mean not too many equations)
- What is CMA-ES?
- How does it work?
- Why is it superior to other strategies?
| What is covariance matrix adaptation evolution strategy? | CC BY-SA 3.0 | null | 2011-04-25T17:17:12.213 | 2017-04-02T11:58:46.580 | null | null | 4319 | [
"covariance-matrix"
] |
9962 | 1 | 9974 | null | 3 | 7709 | I have a multi-class dataset like the following (a,b,c,d are features and e is the class (it can be 0,1 and 2)).
```
a b c d e
1 1 1 2 2 1
2 1 2 4 2 0
3 1 2 4 2 0
4 2 2 2 2 0
5 2 1 2 2 2
```
I am trying to use mlogit package in order to see which column is more important but I am having a difficulty to u... | Multiclass logistic regression with mlogit in R | CC BY-SA 3.0 | null | 2011-04-25T17:52:52.057 | 2011-04-26T00:15:37.927 | 2011-04-25T19:21:31.530 | null | 4320 | [
"r",
"logistic"
] |
9963 | 2 | null | 9947 | 4 | null | Because K-means minimizes variances, a good criterion is to minimize the sum of squared distances between the pairs of points.
This is an [integral (0/1) linear program](http://en.wikipedia.org/wiki/Linear_programming#Integer_unknowns). Specifically, the pairing can be specified by a matrix $\Lambda = (\lambda_{ij})$ ... | null | CC BY-SA 3.0 | null | 2011-04-25T17:53:54.317 | 2011-04-25T17:59:55.590 | 2011-04-25T17:59:55.590 | 919 | 919 | null |
9964 | 2 | null | 9911 | 0 | null | I think you may want to reshape your data, not reduce it. This will let you change the structure of your data set so that you can use all of your observations. You don't mention which statistical package you're using, but R, stata, and MATLAB all have a nice out-of-the-box reshape command you can use.
Side thought: yo... | null | CC BY-SA 3.0 | null | 2011-04-25T18:15:06.373 | 2011-04-25T18:15:06.373 | null | null | 4110 | null |
9965 | 1 | null | null | 1 | 108 | This is probably a simple question.
I'm studying events which have N outcomes, of which exactly one is correct. N is very large, more than a billion (and is known). There are many possible events, some of which are tested multiple times.
I would like to test the following model: a given event is tested correctly with... | Testing a model with truncated data | CC BY-SA 3.0 | null | 2011-04-25T18:52:46.150 | 2011-04-25T18:52:46.150 | null | null | 1378 | [
"hypothesis-testing",
"binomial-distribution",
"censoring"
] |
9966 | 1 | null | null | 1 | 452 | I have a dataset with between 10,000 and 100,000 feature values. The number of datapoints is between 1,000 and 10,000. I want to perform a LASSO on this dataset but can't really find any good software to do so. Does anyone have any suggestions?
| Software for LASSO for high dimensional dataset | CC BY-SA 3.0 | null | 2011-04-25T20:01:37.703 | 2011-04-26T06:40:19.617 | null | null | 4322 | [
"software",
"lasso"
] |
9967 | 2 | null | 9961 | 3 | null | It's an optimization algorithm: it tries to find the minimum of a function. It is said to be amongst the best optimization algorithms for non-convex problems in high dimensions (above 5 or 10 parameters to optimize).
The term Covariance in the name is a bit misleading to the statistics community: there is no statistics... | null | CC BY-SA 3.0 | null | 2011-04-25T20:08:08.723 | 2011-04-25T20:08:08.723 | null | null | 1265 | null |
9969 | 2 | null | 9940 | 1 | null | In the asymptotic sense seemingly suggested by the phasing of the question, it's not true, but the analysis might be revealing.
We don't even need $Z$.
Let $p$ be the chance of a standard normal variable being $c$ or less; that is, $p = \Phi(c)$. Then the chance that at least $k$ or more of the $X_i$ are less than or ... | null | CC BY-SA 3.0 | null | 2011-04-25T20:32:48.797 | 2011-04-25T20:32:48.797 | null | null | 919 | null |
9971 | 1 | null | null | 4 | 1298 |
### Context:
I am trying to analyze an experiment on plant community response to two treatments. Here’s a simplified description of the experiment, there are a few extra complications in reality.
Treatments were applied to small patches of ground arranged in blocks with a mix of naturally occurring plant species ... | Is MANOVA the correct way to handle multiple response variables that are additive? | CC BY-SA 3.0 | null | 2011-04-25T21:38:23.780 | 2011-04-26T05:13:34.833 | 2011-04-26T03:21:51.807 | 183 | 4326 | [
"multivariate-analysis",
"repeated-measures",
"manova"
] |
9972 | 1 | null | null | 1 | 2442 | I'm compiling a survey that will have several questions which would lend to the creation of an index of a main dependent variable (level of engagement in sucession planning). The questions will involve topics like:
- PROCESS: linking strategic planning to succession planning, identifying critical positions that need ... | Getting started with creating an index based on multiple survey items | CC BY-SA 3.0 | null | 2011-04-25T21:54:51.503 | 2011-04-26T14:11:36.723 | 2011-04-26T05:18:32.050 | 183 | 4327 | [
"survey",
"scales"
] |
9973 | 2 | null | 9961 | 8 | null | Per the [wikipedia page](http://en.wikipedia.org/wiki/CMA-ES) linked above to answer (1) this is another form of gradient descent (which if you need more information with lots of pictures there are many articles available if you google it -- sorry apparently new posters only get 2 urls so I'm having to have to tell you... | null | CC BY-SA 3.0 | null | 2011-04-25T23:22:45.657 | 2011-04-25T23:22:45.657 | null | null | 4325 | null |
9974 | 2 | null | 9962 | 3 | null | Multinomial logit assumes that you have a categorical dependent variable. In your case, there are three categories, denoted 0, 1, and 2. You've set 1 as the reference category, which means that mlogit is going to use 1 as the baseline category -- everything else is compared to 1.
The thing to keep in mind is that in ... | null | CC BY-SA 3.0 | null | 2011-04-26T00:15:37.927 | 2011-04-26T00:15:37.927 | null | null | 4110 | null |
9975 | 2 | null | 9926 | 24 | null | Letting $f$ denote a probability density function (either with respect to Lebesgue or counting measure, respectively), the quantity $\newcommand{\rd}{\mathrm{d}}$
$$
H_\alpha(f) = -\frac{1}{\alpha-1} \log(\textstyle\int f^\alpha \rd \mu)
$$
is known as the [Renyi entropy](http://en.wikipedia.org/wiki/R%C3%A9nyi_entr... | null | CC BY-SA 3.0 | null | 2011-04-26T01:36:18.780 | 2011-04-26T01:36:18.780 | null | null | 2970 | null |
9976 | 2 | null | 2914 | 5 | null | I would suggest that this is a problem with how the results are reported. Not to "beat the Bayesian drum" but approaching model uncertainty from a Bayesian perspective as an inference problem would greatly help here. And it doesn't have to be a big change either. If the report simply contained the probability that t... | null | CC BY-SA 3.0 | null | 2011-04-26T01:38:14.393 | 2011-04-26T01:38:14.393 | null | null | 2392 | null |
9977 | 1 | null | null | 6 | 417 | I'm trying to estimate the design effect of a series of relatively small sample size surveys ($n\sim 70$) with multiple responses. Design effects roughly correspond to how much larger actual sample variance than would be expected from naive random sampling. The simplest way to parametrize this is for Effective Sample S... | Modeling multinomial problems with unknown sample size in BUGS | CC BY-SA 3.0 | null | 2011-04-26T03:05:11.570 | 2011-12-30T16:35:45.870 | 2011-04-26T12:00:01.143 | 3911 | 996 | [
"bayesian",
"sampling",
"markov-chain-montecarlo",
"sample-size",
"bugs"
] |
9978 | 2 | null | 9947 | 4 | null | The problem you're trying to solve is a [min-cost matching problem](http://en.wikipedia.org/wiki/Hungarian_algorithm), specifically the problem of minimizing the functional
$F(\pi) = \sum_i \|c_i - k_{\pi(i)}\|^2 $
where $\pi$ is over all permutations in $S_n$.
This can be solved by the Hungarian algorithm (which is ... | null | CC BY-SA 3.0 | null | 2011-04-26T04:29:33.870 | 2011-04-26T04:29:33.870 | null | null | 139 | null |
9979 | 2 | null | 9971 | 2 | null | I can see the merits in running four separate repeated measures ANOVAs. If your theoretical question concerns the four individual variables, then running the ANOVAs separately is more aligned with your theoretical question.
I guess the main issue is the parsimony of your approach and controlling your Type I error rate.... | null | CC BY-SA 3.0 | null | 2011-04-26T05:13:34.833 | 2011-04-26T05:13:34.833 | null | null | 183 | null |
9980 | 2 | null | 9966 | 2 | null | Check the article by Wu Chen Hastie Sobel Lange - Genome-wide association analysis by lasso penalized logistic regression - 2009. They mention a 'swindle' that is not hard to implement + then you can simply work with glmnet (there is a new version out recently which promises a performance improvement but I haven't had ... | null | CC BY-SA 3.0 | null | 2011-04-26T06:40:19.617 | 2011-04-26T06:40:19.617 | null | null | 4257 | null |
9981 | 1 | 9986 | null | 5 | 2538 | In a technique that uses CUSUM for change-point detection in this [paper](http://www.cs.utexas.edu/~mahimkar/MERCURY_sigcomm10.pdf), the first step is given below:
>
Let $x_1, x_2,..., x_n$ be the $n$
samples in an event-series. The
samples are ranked in increasing order
and the rank $r_i$ for each sample is
c... | What is the meaning of rank in the context of change-detection? | CC BY-SA 3.0 | null | 2011-04-26T06:54:27.007 | 2011-04-26T17:52:45.217 | 2011-04-26T17:52:45.217 | 1390 | 2164 | [
"statistical-significance",
"nonparametric",
"change-point"
] |
9982 | 2 | null | 9739 | 0 | null | A nicely documented python library for spatial analysis that has some clustering is [pySAL](http://pysal.org/1.1/library/region/index.html).
Another python library in the development stage that is focused on spatial clustering is [clusterPy](http://www.rise-group.org/risem/clusterpy/clusterPy-pysrc.html) [(pdf slide p... | null | CC BY-SA 3.0 | null | 2011-04-26T07:13:33.340 | 2011-04-26T07:13:33.340 | null | null | 4329 | null |
9983 | 2 | null | 277 | 23 | null | Non-spatial model
My House Value is a function of my home Gardening Investment.
SAR model
My House Value is a function of the House Values of my neighbours.
CAR model
My House Value is a function of the Gardening Investment of my neighbours.
| null | CC BY-SA 3.0 | null | 2011-04-26T07:24:16.177 | 2011-04-26T07:24:16.177 | null | null | 4329 | null |
9984 | 2 | null | 9928 | 3 | null | The definition of fat-tail in [wikipedia](http://en.wikipedia.org/wiki/Fat_tail) is that
$$p(x)\sim x^{-(\alpha+1)}$$
as $x\to\infty$ for some $\alpha>0$. Now
$$\frac{e^x}{x^{\alpha+1}}\to\infty,$$
as $x\to\infty$, so the $Ee^X$ cannot exist for such type of distributions. So you need to precise what do you have in m... | null | CC BY-SA 3.0 | null | 2011-04-26T07:53:57.130 | 2011-04-26T07:53:57.130 | null | null | 2116 | null |
9985 | 2 | null | 9809 | 3 | null | Let's build it!
You mentioned:
1 moment generating function
2 law of iterated expectations
3 change of measure
Adding:
4 Decompose random variable as a sum. Usually the sum of indicators of something.
5 Build a reccurence relation for E(X) (or a set of linear equations). Useful in Markov Chains.
6 Stopping time theorem... | null | CC BY-SA 3.0 | null | 2011-04-26T10:07:42.067 | 2011-04-26T10:29:53.403 | 2011-04-26T10:29:53.403 | 2043 | 2043 | null |
9986 | 2 | null | 9981 | 6 | null | Given your data:
```
cp <- c(5, 2, 4, 1, 9, 2, 9, 2, 10, 1)
```
then the ranks, with ties being given average of the ranks, are:
```
> rank(cp)
[1] 7.0 4.0 6.0 1.5 8.5 4.0 8.5 4.0 10.0 1.5
```
What is being done here? If you sort the data in increasing order, then we have a `1` in both rank order positions ... | null | CC BY-SA 3.0 | null | 2011-04-26T10:42:52.393 | 2011-04-26T10:42:52.393 | null | null | 1390 | null |
9987 | 1 | null | null | 7 | 6531 | I know that "deviations in the data are devil", and when the distribution is highly skewed, it is better to consider median as average rather than mean, but how to decide these hard-limits.
For example:
- CASE 1:
Assume X = 10,20,30,40,50,60,70
In this case, I think that it is better to use mean and that it will give... | When does the amount of skew or prevalence of outliers make the median preferable to the mean? | CC BY-SA 3.0 | null | 2011-04-26T11:35:49.713 | 2017-11-10T23:21:52.083 | 2017-11-10T23:21:52.083 | 128677 | 4331 | [
"mean",
"median"
] |
9988 | 1 | 10019 | null | 4 | 2828 | I received this question by email from a Neuroscience PhD student.
>
I would greatly appreciate if you
could please let me know whether
Factor Analysis could load positively
and inversely correlated variables
onto the same latent factor, whereas
Cluster Analysis can only cluster into
the same factor either... | Can cluster analysis cluster variables that both positively and negatively correlate with each other? | CC BY-SA 3.0 | null | 2011-04-26T11:37:30.257 | 2011-04-27T05:01:49.230 | null | null | 183 | [
"clustering",
"factor-analysis"
] |
9989 | 2 | null | 9987 | 2 | null | You can read about measures of central tendency here: [http://en.wikipedia.org/wiki/Central_tendency](http://en.wikipedia.org/wiki/Central_tendency) .
Generally, you analyse a sample in order to tell something about a (much larger) population. Often you know more about the population than merely the data in your sample... | null | CC BY-SA 3.0 | null | 2011-04-26T11:49:10.857 | 2011-04-26T13:02:21.527 | 2017-04-13T12:44:20.840 | -1 | 3911 | null |
9990 | 1 | 12934 | null | 17 | 4169 | I'm decently familiar with mixed effects models (MEM), but a colleague recently asked me how it compares to latent growth models (LGM). I did a bit of googling, and it seems that LGM is a variant of structural equation modelling that is applied to circumstances where repeated measures are obtained within each level of ... | What are the differences between "Mixed Effects Modelling" and "Latent Growth Modelling"? | CC BY-SA 3.0 | null | 2011-04-26T12:33:47.493 | 2020-01-22T12:59:08.357 | 2020-01-22T12:59:08.357 | 11887 | 364 | [
"mixed-model",
"panel-data",
"growth-model"
] |
9991 | 2 | null | 9987 | 1 | null | Be careful with medians: they are biased estimators and the degree of bias can change depending on the skew of the distribution and the sample size (see [Miller, 1988](http://www.ncbi.nlm.nih.gov/pubmed/2971778)). This means that if you are comparing two conditions that have either different skew or different sample si... | null | CC BY-SA 3.0 | null | 2011-04-26T12:58:56.007 | 2011-04-26T12:58:56.007 | null | null | 364 | null |
9993 | 2 | null | 9987 | 2 | null | There are no hard and fast rules. They convey different information and have different properties. You select the statistic that best conveys what you want to convey. Or better yet, select statistics that best describe the data. Keep this same thing in mind when you're selecting a measure of central tendency to ana... | null | CC BY-SA 3.0 | null | 2011-04-26T13:17:12.987 | 2011-04-26T13:17:12.987 | null | null | 601 | null |
9994 | 2 | null | 9987 | 7 | null |
### Framing the question
- You are asking an applied and subjective question, and thus, any answer needs to be infused with applied and subjective considerations.
- From a purely statistical perspective, the mean and median both provide different information about the central tendency of a sample of data. Thus, ne... | null | CC BY-SA 3.0 | null | 2011-04-26T14:02:40.367 | 2011-04-26T14:02:40.367 | null | null | 183 | null |
9995 | 2 | null | 9972 | 1 | null | Check out some of the following:
- the literature on scale construction.
- discussions of formative and reflective indicators (here's a discussion).
It sounds like your scale might be formative, in that it is driven by theoretical definition of a construct rather than some natural correlation between the items and fa... | null | CC BY-SA 3.0 | null | 2011-04-26T14:11:36.723 | 2011-04-26T14:11:36.723 | 2017-04-13T12:44:44.530 | -1 | 183 | null |
9997 | 1 | 9999 | null | 8 | 2159 | I've been doing some Machine Learning, and have been using k-fold cross-validation to assess the generalisation performance of the algorithm. I've tried k-fold cross-validation with k = 5 and k = 200 and get very different results for Support Vector Machine classification.
```
k SVM accuracy
-----------------
5 7... | Information on how value of k in k-fold cross-validation affects resulting accuracies | CC BY-SA 3.0 | null | 2011-04-26T15:25:45.430 | 2011-04-26T16:54:13.690 | null | null | 261 | [
"machine-learning",
"cross-validation",
"svm"
] |
9998 | 2 | null | 9987 | 1 | null | "deviations in the data are the devil" is just not true I think - well I don't agree with it at least. I'd say its more like "chilli" than the "devil" - as much as you can reasonably handle is good, but it can get nasty if there is too much.
The most general procedure I know of to "choose a statistic" to report your d... | null | CC BY-SA 3.0 | null | 2011-04-26T16:39:30.940 | 2011-04-26T16:39:30.940 | null | null | 2392 | null |
9999 | 2 | null | 9997 | 6 | null | Not much of a "proof" but when k is small, you are removing a much larger chunk of your data, so you model has a much smaller amount of data to "learn from". For k=5 you are removing 20% of the data each time, whereas for k=200 you are only removing 0.5%. You model has a much better chance of picking up all the relev... | null | CC BY-SA 3.0 | null | 2011-04-26T16:54:13.690 | 2011-04-26T16:54:13.690 | null | null | 2392 | null |
10001 | 1 | 10028 | null | 47 | 18651 | I have noticed that there are a few implementations of random forest such as ALGLIB, Waffles and some R packages like `randomForest`. Can anybody tell me whether these libraries are highly optimized? Are they basically equivalent to the random forests as detailed in [The Elements of Statistical Learning](http://statw... | Optimized implementations of the Random Forest algorithm | CC BY-SA 3.0 | null | 2011-04-26T18:39:04.007 | 2021-04-14T12:10:45.183 | 2014-05-17T16:58:30.963 | 27403 | 847 | [
"random-forest",
"algorithms",
"model-evaluation"
] |
10002 | 1 | 10023 | null | 18 | 940 | Given a predicted variable (P), a random effect (R) and a fixed effect (F), one could fit two* mixed effects models ([lme4](http://cran.r-project.org/web/packages/lme4/) syntax):
```
m1 = lmer( P ~ (1|R) + F )
m2 = lmer( P ~ (1+F|R) + F)
```
As I understand it, the second model is the one that permits the fixed effect... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model? | CC BY-SA 3.0 | null | 2011-04-26T19:43:37.553 | 2021-11-11T00:27:23.733 | 2020-07-17T19:08:08.463 | 7486 | 364 | [
"r",
"regression",
"mixed-model",
"lme4-nlme",
"random-effects-model"
] |
10003 | 1 | 11139 | null | 11 | 334 |
## Background
I read about [StatProb.com](http://statprob.com) from a comment on [Andrew Gelman's Blog](http://www.stat.columbia.edu/%7Ecook/movabletype/archives/2011/03/why_edit_wikipe.html#comment-2187431).
According to the website, StatProb is:
>
StatProb: The Encyclopedia Sponsored
by Statistics and Probability... | Is it worthwhile to publish at the refereed wiki StatProb.com? | CC BY-SA 3.0 | null | 2011-04-26T20:09:18.910 | 2019-09-24T19:40:02.243 | 2020-06-11T14:32:37.003 | -1 | 2750 | [
"probability",
"references",
"methodology"
] |
10004 | 2 | null | 10001 | 7 | null | The [ELSII](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) used [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) (see e.g., footnote 3 p.591), which is an R implementation of the Breiman and Cutler's [Fortran code](http://stat-www.berkeley.edu/users/breiman/RandomForests) from Salford.... | null | CC BY-SA 3.0 | null | 2011-04-26T20:12:31.790 | 2011-04-26T20:12:31.790 | null | null | 930 | null |
10005 | 1 | null | null | 0 | 24408 | I have a table I want to convert into a graph (bar-graph or line-graph)
The first column has fixed values. Twenty different values are simulated for these fixed values and kept in the next columns. I want to plot a graph of the fixed column against all the different twenty simulated columns.
How do I go about it?
## E... | How to convert a table into a graph in R | CC BY-SA 3.0 | null | 2011-04-26T20:15:47.633 | 2013-03-29T02:42:39.043 | 2020-06-11T14:32:37.003 | -1 | 4340 | [
"r",
"data-visualization"
] |
10006 | 1 | null | null | 22 | 2452 | Suppose $X\sim \operatorname{InvWishart}(\nu, \Sigma_0)$. I'm interested in the marginal distribution of the diagonal elements $\operatorname{diag}(X) = (x_{11}, \dots, x_{pp})$. There are a few simple results on the distribution of submatrices of $X$ (at least some listed at Wikipedia). From this I can figure that the... | Marginal distribution of the diagonal of an inverse Wishart distributed matrix | CC BY-SA 3.0 | null | 2011-04-26T20:30:43.627 | 2019-10-02T17:32:12.547 | 2016-12-04T10:15:55.690 | 113090 | 26 | [
"distributions",
"probability",
"density-function"
] |
10007 | 2 | null | 6776 | 3 | null | It's a mixture model set up you've got. So to start, put the mixture identifying variable in - you don't have it yet. It's an indicator variable saying whether a case comes from one regression (say Z=0) or the other (say Z=1). Probably it will enter the full model in the form of an interaction with a slope and/or in... | null | CC BY-SA 3.0 | null | 2011-04-26T20:47:11.920 | 2011-04-26T20:47:11.920 | null | null | 1739 | null |
10008 | 1 | null | null | 1 | 1352 | I have a scenario where I have a User which likes 10 different sports and there is another user which likes 20 different sports. I need to find the correlation between them. What kind of correlations can be used in such a scenario. Any kind of guide would be helpful. I tried with Pearson correlation but was not helpful... | Need to find correlation between two entities | CC BY-SA 3.0 | null | 2011-04-26T20:48:43.323 | 2011-04-27T13:34:20.870 | 2011-04-27T13:34:20.870 | null | 4341 | [
"correlation",
"matlab"
] |
10009 | 2 | null | 10005 | 1 | null | Try this:
```
#Generate 100 x values. Then generate 20 random walk y values for each x value
x <- seq(1, 100, 1)
y <- matrix(20*100, nrow=100, ncol=20)
for (i in 1:20) {
y[, i] <- cumsum(rnorm(100))
}
#Build the table
df <- data.frame(x=x, y=y)
head(df)
#Plot the table
matplot(df[, 1], df[, 2:21]... | null | CC BY-SA 3.0 | null | 2011-04-26T21:56:07.137 | 2011-04-26T22:01:25.727 | 2011-04-26T22:01:25.727 | 2775 | 2775 | null |
10010 | 1 | null | null | 4 | 191 | I am currently trying to do the following in R:
I have thousands of measured spectra (x,y; see below). Each spectra has one or two peaks. Also I have sets of "training" spectra obtained in more controlled conditions and I would like to know which of my training spectra has the closest match to the measured spectra!?
I... | Classifying spectra | CC BY-SA 3.0 | null | 2011-04-26T22:27:58.763 | 2012-05-10T05:23:07.890 | 2012-05-09T10:31:02.793 | 4479 | 4342 | [
"classification"
] |
10011 | 1 | null | null | 7 | 1268 | I have a set of samples in which I assume there are 2 definite subsets in it. I plotted their values in a histogram and found that there are two distinct modes as shown in the figure below.
My question is how do I differentiate two groups. i.e how do I choose a value that differentiates the two subsets?
![enter image ... | How to differentiate two subgroups from a histogram? | CC BY-SA 3.0 | null | 2011-04-26T22:36:32.657 | 2013-05-13T04:58:16.037 | 2013-05-13T04:58:16.037 | 805 | 2725 | [
"normal-distribution",
"mixture-distribution",
"histogram",
"unsupervised-learning"
] |
10012 | 2 | null | 10008 | 1 | null | This is like [inter-rater reliability](http://en.wikipedia.org/wiki/Inter-rater_reliability). The code [here](http://www.mathworks.com/matlabcentral/fileexchange/15365) should do it.
| null | CC BY-SA 3.0 | null | 2011-04-27T00:13:44.980 | 2011-04-27T00:13:44.980 | null | null | 3874 | null |
10013 | 1 | null | null | 5 | 2310 | I found the following posts interesting and I was wondering if any of you guys know of good academic papers that describe methods/relationships of exogenous variables in VECM models. If so could you kindly point them out to me as I am very interested in learning. Thank you.
[Finding coefficients for VECM + exogenous va... | Exogenous variables in VECM | CC BY-SA 3.0 | null | 2011-04-27T00:16:30.820 | 2021-11-21T01:53:36.777 | 2021-11-21T01:53:36.777 | 11887 | 4338 | [
"time-series",
"exogeneity"
] |
10015 | 2 | null | 8642 | 1 | null | I have two notes and one suggestion.
The first note is that testing theory is typically done by setting an acceptable level where you would reject a true hypothesis (Type I error), then minimize the risk of accepting a false hypothesis (Type II error). There are two reasons for this, first is that all your tests use th... | null | CC BY-SA 4.0 | null | 2011-04-27T01:51:25.523 | 2023-05-09T17:07:10.133 | 2023-05-09T17:07:10.133 | 31853 | 2339 | null |
10016 | 2 | null | 10011 | 0 | null | If you are willing to assume the populations have the same variance you could use essentially LDA without the normality assumption (a.k.a. Fisher's Method or Fisher's Discriminant Function).
Without this assumption you could try an EM algorithm which is indirectly what Matt Suggested since this would be a mixture model... | null | CC BY-SA 3.0 | null | 2011-04-27T02:14:35.040 | 2011-04-27T02:14:35.040 | null | null | 2339 | null |
10017 | 1 | 60262 | null | 44 | 71963 | I am trying to understand standard error "clustering" and how to execute in R (it is trivial in Stata). In R I have been unsuccessful using either `plm` or writing my own function. I'll use the `diamonds` data from the `ggplot2` package.
I can do fixed effects with either dummy variables
```
> library(plyr)
> library(g... | Standard error clustering in R (either manually or in plm) | CC BY-SA 3.0 | null | 2011-04-27T02:34:11.373 | 2023-04-12T09:48:21.233 | 2016-02-20T16:36:10.930 | 36515 | 1445 | [
"r",
"panel-data",
"standard-error",
"fixed-effects-model",
"clustered-standard-errors"
] |
10019 | 2 | null | 9988 | 4 | null |
### General interpration of question:
The question is a bit confusing, but I interpret it as follows.
- Factor Analysis: When a survey has multiple items and some are positively worded (e.g., "I am the life of the party") and others are negatively worded (e.g., "I avoid social interaction"), factor analysis often a... | null | CC BY-SA 3.0 | null | 2011-04-27T05:01:49.230 | 2011-04-27T05:01:49.230 | null | null | 183 | null |
10020 | 1 | null | null | 14 | 3402 | I was advising a research student with a particular problem, and I was keen to get the input of others on this site.
### Context:
The researcher had three types of predictor variables. Each type contained a different number of predictor variables. Each predictor was a continuous variable:
- Social: S1, S2, S3, S4 ... | Comparing importance of different sets of predictors | CC BY-SA 3.0 | null | 2011-04-27T05:35:46.223 | 2020-04-02T16:58:41.300 | 2011-04-27T08:57:44.160 | 183 | 183 | [
"regression",
"predictor",
"importance"
] |
10021 | 1 | 10025 | null | 2 | 324 | Given a Gaussian distribution $N(\mu_1,\sigma_1^2)$, i would like to choose another mean $\mu_2$ which is $2\sigma_1$ away from $\mu_1$.
In this case our new mean $\mu_2=\mu_1\pm 2\sigma_1$.
How do we calculate the new mean($\mu_2$) in multivariate case?
I mean to say, when your multivariate Gaussian distribution is ... | New mean calculation in multivariate gaussian | CC BY-SA 3.0 | null | 2011-04-27T10:07:39.523 | 2011-06-26T13:42:29.627 | 2011-06-26T13:42:29.627 | null | 4290 | [
"multivariate-analysis"
] |
10022 | 2 | null | 10020 | 7 | null | Importance
First thing to do is operationalise 'importance of predictors'. I shall assume that it means something like 'sensitivity of mean outcome to changes in predictor values'. Since your predictors are grouped then sensitivity of the mean outcome to groups of predictors is more interesting than a variable by var... | null | CC BY-SA 3.0 | null | 2011-04-27T10:48:27.033 | 2011-04-27T10:48:27.033 | null | null | 1739 | null |
10023 | 2 | null | 10002 | 13 | null | I am not an expert in mixed effect modelling, but the question is much easier to answer if it is rephrased in hierarchical regression modelling context. So our observations have two indexes $P_{ij}$ and $F_{ij}$ with index $i$ representing class and $j$ members of the class. The hierarchical models let us fit linear re... | null | CC BY-SA 4.0 | null | 2011-04-27T11:12:22.073 | 2021-11-11T00:27:23.733 | 2021-11-11T00:27:23.733 | 42597 | 2116 | null |
10024 | 1 | 10041 | null | 10 | 2538 | According to [wiki](http://en.wikipedia.org/wiki/Cluster_analysis#Partitional_clustering) the most widely used convergence criterion is "assigment hasn't changed". I was wondering whether cycling can occur if we use such convergence criterion? I'd be pleased if anyone pointed a reference to an article that gives an exa... | Cycling in k-means algorithm | CC BY-SA 3.0 | null | 2011-04-27T11:13:57.890 | 2013-03-16T23:57:51.583 | null | null | 1643 | [
"clustering",
"algorithms",
"k-means"
] |
10025 | 2 | null | 10021 | 2 | null | In the bivariate case you can substitute the two points ($\mu_2=\mu_1\pm 2\sigma_1$) with an isodensity ellipse: [http://www.stat.psu.edu/online/courses/stat505/05_multnorm/06_multnorm_revist.html](http://www.stat.psu.edu/online/courses/stat505/05_multnorm/06_multnorm_revist.html) .
Your $2\sigma_1$ criterion seems a b... | null | CC BY-SA 3.0 | null | 2011-04-27T11:35:54.217 | 2011-04-27T11:35:54.217 | null | null | 3911 | null |
10026 | 2 | null | 10020 | 8 | null | Suppose that the first set of predictors requires $a$ degrees of freedom ($a \geq 4$ allowing for nonlinear terms), the second set requires $b$, and the third requires $c$ ($c \geq 3$) allowing for nonlinear terms. Compute the likelihood ratio $\chi^2$ test for the combined partial effects of each set, yielding $L_{1},... | null | CC BY-SA 4.0 | null | 2011-04-27T11:43:16.597 | 2020-04-02T16:58:41.300 | 2020-04-02T16:58:41.300 | 21054 | 4253 | null |
10027 | 2 | null | 10011 | 3 | null | I assume you are talking about Neonatal Behavioral Assessment Scale values in Hereditary Renal Adysplasia.
I often see in medical research that physicians want to have cut-offs and simple threshold based interpretations of their research results, based merely on the distribution of the measurements. Practice and appli... | null | CC BY-SA 3.0 | null | 2011-04-27T11:59:18.457 | 2011-04-27T11:59:18.457 | null | null | 3911 | null |
10028 | 2 | null | 10001 | 33 | null | (Updated 6 IX 2015 with suggestions from comments, also made CW)
There are two new, nice packages available for R which are pretty well optimised for a certain conditions:
- ranger -- C++, R package, optimised for $p>>n$ problems, parallel, special treatment of GWAS data.
- Arborist -- C++, R and Python bindings, opt... | null | CC BY-SA 4.0 | null | 2011-04-27T12:02:47.953 | 2021-04-14T12:10:45.183 | 2021-04-14T12:10:45.183 | -1 | null | null |
10029 | 2 | null | 10008 | 1 | null | If your goal is to measure similarity between individual users or groups of users you may use similarity or distance measures used in cluster analysis, biclustering or multidimensional scaling. In situations where you need such a measure the above techniques themselves may be useful, too.
| null | CC BY-SA 3.0 | null | 2011-04-27T12:11:40.880 | 2011-04-27T12:11:40.880 | null | null | 3911 | null |
10030 | 1 | null | null | 4 | 2993 | On the Internet there is an example of k-s test being applied relative to distribution of number of bird varieties over different five hour periods.
The observed distribution was:
```
a=c(0,1,1,9,4)
```
The expected distribution (if there is no difference between the five hours) could be:
```
b=c(3,3,3,3,3)
```
After... | Difference between K-S manual test and K-S test with R? | CC BY-SA 3.0 | null | 2011-04-27T12:34:12.893 | 2011-05-02T13:55:08.293 | 2011-05-02T13:55:08.293 | 183 | 4345 | [
"r",
"kolmogorov-smirnov-test"
] |
10031 | 1 | 10034 | null | 3 | 267 | I have a multinomial logistic regression model.
One of the output categories is not observed in the data set that I'm using.
### Example:
- 4 different diagnoses (response variable) in the population, but in the sample, Type 3 never occurred
- 5 hormone level measurements (predictors)
### Question
- What ... | How to handle categorical dependent variable using logistic regression when one of the categories never occurs in the sample | CC BY-SA 3.0 | null | 2011-04-27T13:19:43.057 | 2013-11-15T13:03:53.140 | 2011-04-28T15:18:55.860 | 183 | 3280 | [
"logistic"
] |
10032 | 2 | null | 10030 | 6 | null | You are testing a different thing.
While you think `c(0,1,1,9,4)` means you are looking at 0 values of one, 1 value of two, 1 value of three, 9 values of four, and 4 values of five, R thinks you are looking at one value of 0, two values of 1, one value of 9, and one value of 4.
To get D = 0.4667..., try the rather ve... | null | CC BY-SA 3.0 | null | 2011-04-27T14:08:24.743 | 2011-04-27T14:08:24.743 | null | null | 2958 | null |
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