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2013 Weighted least squares estimation with missing responses: An empirical likelihood approach
Anton Schick
Electron. J. Statist. 7: 932-945 (2013). DOI: 10.1214/13-EJS793

Abstract

A heteroscedastic linear regression model is considered where responses are allowed to be missing at random. An estimator is constructed that matches the performance of the weighted least squares estimator without the knowledge of the conditional variance function. This is usually done by constructing an estimator of the variance function. Our estimator is a maximum empirical likelihood estimator based on an increasing number of estimated constraints and avoids estimating the variance function.

Citation

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Anton Schick. "Weighted least squares estimation with missing responses: An empirical likelihood approach." Electron. J. Statist. 7 932 - 945, 2013. https://doi.org/10.1214/13-EJS793

Information

Published: 2013
First available in Project Euclid: 3 April 2013

zbMATH: 1336.62092
MathSciNet: MR3044504
Digital Object Identifier: 10.1214/13-EJS793

Subjects:
Primary: 62F12, 62G05
Secondary: 62J05

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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