Non-parametric maximum likelihood estimation of censored regression models
type: Item
instance of: scholarly article
title: Non-parametric maximum likelihood estimation of censored regression models
zbMATH Open document ID: 0604.62034
DOI: 10.1016/0304-4076(86)90011-4
author: B. George
published in: Journal of Econometrics
publication date: 1986-01-01T00:00:00Z
review text: This paper presents a consistent estimator of a censored linear regression model which does not require knowledge of the distribution of the error term. The estimator considered here applies \textit{G. Duncan}'s [A robust regression estimator. Dept. Econ. Working Paper, Washington State Univ., Pullman/WA (1982)] suggestion that the likelihood function for the censored regression model be treated as a functional of both the unknown regression vector and the unknown error distribution. Our estimator is the majorizing regression vector for this non-parametric likelihood functional. We find conditions which ensure the consistency of the NPMLE. The paper concludes with the results of Monte Carlo experiments which show the NPMLE to be more efficient than Powell's least absolute deviations (LAD) estimator [\textit{J. L. Powell}, J. Econ. 25, 303-325 (1984; Zbl 0571.62100)], particularly when the fraction of censored observations is large and the sample size is small.
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 3976100
zbMATH Keywords: asymptotic normality
zbMATH Keywords: relative efficiency
zbMATH Keywords: consistent estimator
zbMATH Keywords: censored linear regression model
zbMATH Keywords: majorizing regression vector
zbMATH Keywords: non-parametric likelihood functional
zbMATH Keywords: consistency
zbMATH Keywords: Monte Carlo experiments
zbMATH Keywords: Powell's least absolute deviations
MaRDI profile type: Publication
full work available at URL: 0304-4076(86)90011-4
OpenAlex ID: W1988896174
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description: scientific article; zbMATH DE number 3976100