Doubly reweighted estimators for the parameters of the multivariate t-distribution
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, cilt.47, sa.19, ss.4751-4771, 2018 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 47 Sayı: 19
- Basım Tarihi: 2018
- Doi Numarası: 10.1080/03610926.2018.1445861
- Dergi Adı: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.4751-4771
- Anahtar Kelimeler: EM algorithm, ML, MLq, multivariate-t, REGRESSION-MODELS, ROBUST ESTIMATION, EM ALGORITHM, LIKELIHOOD, OUTLIERS
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
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.