Czasopisma Naukowe w Sieci (CNS)

Minimax estimation of the mean matrix of the matrix-variate normal distribution

  1. S. Zinodiny
  2. S. Rezaei
  3. Saralees Nadarajah

Abstract

In this paper, the problem of estimating the mean matrix Θ of a matrix-variate normal distribution with the covariance matrix V Im is considered under the loss functions,
ω tr((δ-X)'Q(δ-X))+(1-ω)tr((δ-Θ)'Q(δ-Θ)) and k[1-e-tr((δ-Θ)'Γ^(-1)(δ-Θ))]. We construct a class of empirical Bayes estimators which are better than the maximum likelihood estimator under the first loss function for m > p + 1 and hence show that the maximum likelihood estimator is inadmissible. For the case Q = V = Ip, we find a general class of minimax estimators. Also we give a class of estimators that improve on the maximum likelihood estimator under the second loss function for m > p + 1 and hence show that the maximum likelihood estimator is inadmissible.

Pobierz artykuł

Ten artykuł

Probability and Mathematical Statistics

36, z. 2, 2016

Strony od 187 do 200

Inne artykuły autorów

Google Scholar

zamknij

Twoj koszyk (produkty: 0)

Brak produktów w koszyku

Twój koszyk Do kasy