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December, 1985 Covariate Measurement Error in Logistic Regression
Leonard A. Stefanski, Raymond J. Carroll
Ann. Statist. 13(4): 1335-1351 (December, 1985). DOI: 10.1214/aos/1176349741

Abstract

In a logistic regression model when covariates are subject to measurement error the naive estimator, obtained by regressing on the observed covariates, is asymptotically biased. We introduce a bias-adjusted estimator and two estimators appropriate for normally distributed measurement errors -a functional maximum likelihood estimator and an estimator which exploits the consequences of sufficiency. The four proposals are studied asymptotically under conditions which are appropriate when the measurement error is small. A small Monte Carlo study illustrates the superiority of the measurement-error estimators in certain situations.

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Leonard A. Stefanski. Raymond J. Carroll. "Covariate Measurement Error in Logistic Regression." Ann. Statist. 13 (4) 1335 - 1351, December, 1985. https://doi.org/10.1214/aos/1176349741

Information

Published: December, 1985
First available in Project Euclid: 12 April 2007

zbMATH: 0582.62061
MathSciNet: MR811496
Digital Object Identifier: 10.1214/aos/1176349741

Subjects:
Primary: 62J05
Secondary: 62H25

Rights: Copyright © 1985 Institute of Mathematical Statistics

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Vol.13 • No. 4 • December, 1985
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