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A Generalization of Rao's Covariance Structure with Applications to Several Linear Models

✍ Scribed by Hiroshi Kurata

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
185 KB
Volume
67
Category
Article
ISSN
0047-259X

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✦ Synopsis

This paper presents a generalization of Rao's covariance structure. In 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.

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