I urgently need some help in my problem for my MS thesis. I have two datasets of same variable dimension but different number of observations, ie same # of columns but not same # rows. The variables are indentical for both sets. I want to compare the multivariate distributions of the two data sets. I have done some google research on the matter and all I could find are tests for normality of multivariate samples. Although that information is also useful, i am more interested in comparing my two datasets whatever their distrubutions maybe. In what way should i compare them? What are the parameters of comparison?. In a mutivariate normal distribution, I have read from an old paper (1983) by Hannu Oja that the eigenvalues of the covariane matrix is a measure of spread or scatterness of the data, or so i understood it that way. Please comfirm this if i am right or wrong. This is as far as my search for answers could go. I want to have paramenters of comparison even if my data sets are not normal. Since I am dealing with two matrices, I also welcome suggestions from the mathematical point of view as well. Thank you very much in advance for any and all suggestions.(adsbygoogle = window.adsbygoogle || []).push({});

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# Comparing two multivariate distributions (two matrices)

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