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

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) algorit...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 47; no. 19; pp. 4751 - 4771
Main Authors Doğru, Fatma Zehra, Bulut, Y. Murat, Arslan, Olcay
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.10.2018
Taylor & Francis Ltd
Subjects
00-01, 99-00
Algorithms
Computer simulation
EM algorithm
Estimating techniques
Maximum likelihood estimates
Maximum likelihood estimation
Maximum likelihood estimators
MLq
multivariate-t
Parameter estimation
Parameter modification
Parameter robustness
Probability distribution
Statistical analysis
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Summary: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.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2018.1445861