Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
成果类型：
Article
署名作者：
Pourahmadi, M
署名单位：
Northern Illinois University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/87.2.425
发表日期：
2000
页码：
425435
关键词：
摘要：
The positive-definiteness constraint is the most awkward stumbling block in modelling the covariance -matrix. Pourahmadi's (1999) unconstrained parameterisation models covariance using covariates in a similar manner to mean modelling in generalised linear models. The new covariance parameters have statistical interpretation as the regression coefficients and logarithms of prediction error variances corresponding to regressing a response on its predecessors. In this paper, the maximum likelihood estimators of the parameters of a generalised linear model for the covariance matrix, their consistency and their asymptotic normality are studied when the observations are normally distributed. These results along with the likelihood ratio test and penalised likelihood criteria such as BIC for model and variable selection are illustrated using a real dataset.