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

Dogru F. Z., BULUT Y. M., ARSLAN O.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.47, no.19, pp.4751-4771, 2018 (SCI-Expanded) identifier identifier


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.