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I have two dataset that i want to compare. each dataset contain the weight of 10 different person measured for 3 different day. I am interested in measuring the probabily that the two sample originate from the same population. People seem to suggest doing a Kolmogorov-Smirnov test but i need a measurement. I was thinking doing the EMD to compare the distribution for each day EMD(dataset1-day1,dataset2-day1) + EMD(dataset1-day2,dataset2-day2) + EMD(dataset1-day3,dataset2-day3) But i could probably take each person as a 3d datapoint and do the EMD in 3d. One other possibility was to do the Hausdorff distance but doing the average of the distance for each point instead of taking the maximum distance. What are the main difference between the two technique. |
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I don't think you have justified exactly why you can't use the KS test. There is a two-sample version which is exactly what you need, see here. it's not that i can't use KS-distance, it's that i dont know why i should use it rather then Kullback–Leibler divergence or anything else.
(Jul 05 '11 at 16:33)
maxime caron
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