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September, 1983 Minimum Distance Estimation in a Linear Regression Model
H. Koul, T. DeWet
Ann. Statist. 11(3): 921-932 (September, 1983). DOI: 10.1214/aos/1176346258

Abstract

This paper discusses a class of minimum distance Cramer-Von Mises type estimators of the slope parameter in a linear regression model. These estimators are obtained by minimizing an integral of squared difference between weighted empiricals of the residuals and their expectations with respect to a large class of integrating measures. The estimator corresponding to the weights proportional to the design variable is shown to be asymptotically efficient within the class at a given error distribution. The paper also discusses the asymptotic null distribution of a class of minimum Cramer-Von Mises type goodness-of-fit test statistics.

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H. Koul. T. DeWet. "Minimum Distance Estimation in a Linear Regression Model." Ann. Statist. 11 (3) 921 - 932, September, 1983. https://doi.org/10.1214/aos/1176346258

Information

Published: September, 1983
First available in Project Euclid: 12 April 2007

zbMATH: 0521.62023
MathSciNet: MR707942
Digital Object Identifier: 10.1214/aos/1176346258

Subjects:
Primary: 62F10
Secondary: 62F03, 62J05

Rights: Copyright © 1983 Institute of Mathematical Statistics

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Vol.11 • No. 3 • September, 1983
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