A generalization of Rao's covariance structure with applications to several linear models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:67

Article

Kurata, H

关键词: Gauss-Markov estimator;    ordinary least squares estimator;    Rao's covariance structure;    seemingly unrelated regression model;    general multivariate analysis of variance model;   

DOI  :  10.1006/jmva.1998.1771

来源: Elsevier

【 摘 要 】

This paper presents a generalization of Rao's covariance structure. Tn a general linear regression model, we classify the error covariance structure into several categories and investigate the efficiency of the ordinary least squares estimator (OLSE) relative to the Gauss-Markov estimator (GME). The classification criterion considered here is the rank of the covariance matrix of the difference between the OLSE and the GME. Hence our classification includes Rao's covariance structure. The results are applied to models with special structures: a general multivariate analysis of variance model, a seemingly unrelated regression model, and a serial correlation model. (C) 1998 Academic Press.

【 授权许可】

Free