Doubly reweighted estimators for the parameters of the multivariate t-distribution |
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| Authors: | Fatma Zehra Do?ru Y Murat Bulut Olcay Arslan |
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| Institution: | 1. Department of Econometrics, Giresun University, Giresun, Turkeyfatma.dogru@giresun.edu.tr;3. Department of Statistics, Eski?ehir Osmangazi University, Eski?ehir, Turkey;4. Department of Statistics, Ankara University, Ankara, Turkey |
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| 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 Ferrari, D., and Y. Yang. 2010. Maximum lq-likelihood estimation. The Annals of Statistics 38 (2):753–83.Crossref], Web of Science ®] , Google Scholar]) 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. |
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| Keywords: | EM algorithm ML MLq multivariate-t |
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