Electronic Journal of Statistics

Weighted least squares estimation with missing responses: An empirical likelihood approach

Anton Schick

Full-text: Open access

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.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 932-945.

Dates
First available in Project Euclid: 3 April 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1364994253

Digital Object Identifier
doi:10.1214/13-EJS793

Mathematical Reviews number (MathSciNet)
MR3044504

Zentralblatt MATH identifier
1336.62092

Subjects
Primary: 62F12: Asymptotic properties of estimators 62G05: Estimation
Secondary: 62J05: Linear regression

Keywords
Heteroscedastic linear regression missing at random maximum empirical likelihood estimation estimated constraints increasing number of constraints efficiency

Citation

Schick, Anton. Weighted least squares estimation with missing responses: An empirical likelihood approach. Electron. J. Statist. 7 (2013), 932--945. doi:10.1214/13-EJS793. https://projecteuclid.org/euclid.ejs/1364994253


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