论文信息 - Least Squares Estimation When the Covariance Matrix and Parameter Vector are Functionally Related

Least Squares Estimation When the Covariance Matrix and Parameter Vector are Functionally Related

Abstract Estimation for the linear model y = Xβ + e with unknown diagonal covariance matrix G is considered. The diagonal elements of G are assumed to be known functions of the explanatory variables X and an unknown parameter vector Θ, where Θ is permitted to contain elements of β. A weighted joint least squares estimator is developed that is asymptotically equivalent to the maximum likelihood estimator. Asymptotic properties of the simple least squares estimator and of the weighted joint least squares estimator are obtained. A sampling experiment is used to compare the estimators.

Wayne A. Fuller | J. D. Jobson | W. Fuller | J. Jobson

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