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

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  • Kurata, Hiroshi
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    Abstract

    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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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 67 (1998)
    Issue (Month): 2 (November)
    Pages: 297-305

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    Handle: RePEc:eee:jmvana:v:67:y:1998:i:2:p:297-305

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    Related research

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

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