An empirical goodness-of-fit test for multivariate distributions

M.P. Mc Assey

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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. © 2013 Copyright Taylor and Francis Group, LLC.
Original languageEnglish
Pages (from-to)1120-1131
Number of pages12
JournalJournal of Applied Statistics
Volume40
Issue number5
DOIs
Publication statusPublished - 2013

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