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

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  • Fatma Zehra Doğru
  • Y. Murat Bulut
  • Olcay Arslan

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.

Suggested Citation

  • Fatma Zehra Doğru & Y. Murat Bulut & Olcay Arslan, 2018. "Doubly reweighted estimators for the parameters of the multivariate t-distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(19), pages 4751-4771, October.
  • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:19:p:4751-4771
    DOI: 10.1080/03610926.2018.1445861
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    Cited by:

    1. Yeşim Güney & Y. Tuaç & Ş. Özdemir & O. Arslan, 2021. "Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 47-74, January.

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