Timeline for Testing for equality of multivariate distributions
Current License: CC BY-SA 3.0
8 events
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| Jul 1, 2017 at 15:27 | comment | added | Stéphane Laurent | duplicate of stats.stackexchange.com/questions/30687/… | |
| Jan 19, 2014 at 8:23 | comment | added | tchakravarty | You might want to follow the literature cited in this recent JMLR paper. In particular, the Rosenbaum paper, and the Hall and Tajvidi paper look interesting (I have not read them). | |
| Jan 10, 2014 at 23:56 | answer | added | Samuel Benidt | timeline score: 2 | |
| Jan 10, 2014 at 5:14 | history | edited | guy | CC BY-SA 3.0 |
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| Jan 10, 2014 at 5:04 | comment | added | guy | @AlecosPapadopoulos yes, I understand that. That's the point of the question :) I want a test that looks at the joint rather than some finite collection of marginals. In principle, I could get away with checking $t^T X = t^T Y$ in distribution, which is why I've also been checking other marginals like the principal components as additional sanity checks. | |
| Jan 10, 2014 at 3:11 | comment | added | Alecos Papadopoulos | Any given set of marginals is compatible with any number of joint distributions. Think of it as the "indeterminacy principle" in the field of Statistics. Consider the simplest case of two dependent normal random variables, that are, moreover, jointly normally distributed. Their correlation coefficient does not appear in their marginals, neither does it affect the mean and variance of each. So for the same two marginal normals, even if you restrict attention to them being jointly normally distributed, their joint distribution can be any bivariate normal with correlation coefficient in $(-1,1)$. | |
| Jan 10, 2014 at 1:24 | history | edited | guy | CC BY-SA 3.0 |
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| Jan 10, 2014 at 1:12 | history | asked | guy | CC BY-SA 3.0 |