Maximum likelihood estimation of covariance matrices under simple tree ordering

✍ Scribed by Ming-Tien Tsai

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 258 KB
• Volume: 89
• Category: Article
• ISSN: 0047-259X
• DOI: 10.1016/s0047-259x(03)00094-0

✦ Synopsis

The closed-form maximum likelihood estimators for the completely balanced multivariate one-way random effect model are obtained by Anderson et al. (Ann. Statist. 14 (1986) 405). It remains open whether there exist the closed-form maximum likelihood estimators for the more general completely balanced multivariate multi-way random effects models. In this paper, a new parameterization technique for covariance matrices is used to grasp the inside structure of likelihood function so that the maximum likelihood equations can be dramatically simplified. As such we obtain the closed-form maximum likelihood estimators of covariance matrices for Wishart density functions over the simple tree ordering set, which can then be applied to get the maximum likelihood estimators for the completely balanced multivariate multi-way random effects models without interactions.