Doubly reweighted estimators for the parameters of the multivariate t-distribution

Fatma Zehra Doğru, Y. Murat Bulut and Olcay Arslan

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 19, 4751-4771

Abstract: The t-distribution (univariate and multivariate) has many useful applications in robust statistical analysis. The parameter estimation of the t-distribution is carried out using maximum likelihood (ML) estimation method, and the ML estimates are obtained via the Expectation-Maximization (EM) algorithm. In this article, we will use the maximum Lq-likelihood (MLq) estimation method introduced by Ferrari and Yang (2010) to estimate all the parameters of the multivariate t-distribution. We modify the EM algorithm to obtain the MLq estimates. We provide a simulation study and a real data example to illustrate the performance of the MLq estimators over the ML estimators.

Date: 2018
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DOI: 10.1080/03610926.2018.1445861

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