期刊名称：Sankhya. Series B, applied and interdisciplinary statistics
印刷版ISSN：0976-8386
电子版ISSN：0976-8394
出版年度：2017
卷号：79
期号：2
页码：292-315
DOI：10.1007/s13571-016-0119-5
语种：English
出版社：Indian Statistical Institute
摘要：AbstractUnivariate Birnbaum-Saunders distribution has received a considerable attention in recent years. Rieck and Nedelman (Technometrics, vol. 33, 51–60, 1991) introduced a log Birnbaum-Saunders distribution. We introduce a multivariate log Birnbaum-Saunders distribution and discuss its different properties. It is observed that the proposed multivariate model can be obtained from the multivariate Gaussian copula. We have proposed the maximum likelihood estimators of the unknown parameters. Since it is a computationally challenging problem, particularly if the dimension is high, we have considered the approximate maximum likelihood estimators based on the Copula structure using two-step procedure. The asymptotic distributions of both these estimators have been obtained. We compare their performances using Monte Carlo simulations, and it is observed that their performances are very similar in nature. One data set has been analyzed for illustrative purposes.
关键词：KeywordsEnBirnbaum-Saunders distributionGaussian copulaFisher information matrixMaximum likelihood estimatorsShannon entropy
