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 language | English |
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| Pages (from-to) | 1120-1131 |
| Number of pages | 12 |
| Journal | Journal of Applied Statistics |
| Volume | 40 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2013 |