Multivariate extreme value analysis and its relevance in a metallographical application

A.B. Schmiedt, H.H. Dickert, W. Bleck and U. Kamps

Journal of Applied Statistics, 2014, vol. 41, issue 3, 582-595

Abstract: Motivated from extreme value (EV) analysis for large non-metallic inclusions in engineering steels and a real data set, the benefit of choosing a multivariate EV approach is discussed. An extensive simulation study shows that the common univariate setup may lead to a high proportion of mis-specifications of the true EV distribution, as well as that the statistical analysis is considerably improved when being based on the respective data of r largest observations, with r appropriately chosen. Results for several underlying distributions and various values of r are presented along with effects on estimators for the parameters of the generalized EV family of distributions.

Date: 2014
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DOI: 10.1080/02664763.2013.845872

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