An empirical goodness-of-fit test for multivariate distributions

Michael McAssey

Journal of Applied Statistics, 2013, vol. 40, issue 5, 1120-1131

Abstract: An empirical test is presented as a tool for assessing whether a specified multivariate probability model is suitable to describe the underlying distribution of a set of observations. This test is based on the premise that, given any probability distribution, the Mahalanobis distances corresponding to data generated from that distribution will likewise follow a distinct distribution that can be estimated well by means of a large sample. We demonstrate the effectiveness of the test for detecting departures from several multivariate distributions. We then apply the test to a real multivariate data set to confirm that it is consistent with a multivariate beta model.

Date: 2013
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DOI: 10.1080/02664763.2013.780160

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