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September, 1978 Estimation for a Linear Regression Model with Unknown Diagonal Covariance Matrix
Wayne A. Fuller, J. N. K. Rao
Ann. Statist. 6(5): 1149-1158 (September, 1978). DOI: 10.1214/aos/1176344317

Abstract

A method of estimating the parameters of a linear regression model when the covariance matrix is an unknown diagonal matrix is investigated. It is assumed that the observations fall into $k$ groups with constant error variance for a group. The estimation is carried out in two steps, the first step being an ordinary least squares regression. The least squares residuals are used to estimate the covariance matrix and the second step is the calculation of the generalized least squares estimator using the estimated covariance matrix. The large sample properties of the estimator are derived for increasing $k$, assuming the numbers in the groups form a fixed sequence.

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Wayne A. Fuller. J. N. K. Rao. "Estimation for a Linear Regression Model with Unknown Diagonal Covariance Matrix." Ann. Statist. 6 (5) 1149 - 1158, September, 1978. https://doi.org/10.1214/aos/1176344317

Information

Published: September, 1978
First available in Project Euclid: 12 April 2007

zbMATH: 0388.62064
MathSciNet: MR499579
Digital Object Identifier: 10.1214/aos/1176344317

Subjects:
Primary: 62J05

Keywords: Linear regression , Two step estimators , unknown variances , weighted least squares

Rights: Copyright © 1978 Institute of Mathematical Statistics

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Vol.6 • No. 5 • September, 1978
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