The Annals of Statistics

Estimation for a Linear Regression Model with Unknown Diagonal Covariance Matrix

Wayne A. Fuller and J. N. K. Rao

Full-text: Open access

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.

Article information

Source
Ann. Statist., Volume 6, Number 5 (1978), 1149-1158.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344317

Digital Object Identifier
doi:10.1214/aos/1176344317

Mathematical Reviews number (MathSciNet)
MR499579

Zentralblatt MATH identifier
0388.62064

JSTOR
links.jstor.org

Subjects
Primary: 62J05: Linear regression

Keywords
Two step estimators linear regression unknown variances weighted least squares

Citation

Fuller, Wayne A.; Rao, J. N. K. Estimation for a Linear Regression Model with Unknown Diagonal Covariance Matrix. Ann. Statist. 6 (1978), no. 5, 1149--1158. doi:10.1214/aos/1176344317. https://projecteuclid.org/euclid.aos/1176344317


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