The Annals of Statistics

Covariate Measurement Error in Logistic Regression

Leonard A. Stefanski and Raymond J. Carroll

Full-text: Open access

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.

Article information

Source
Ann. Statist. Volume 13, Number 4 (1985), 1335-1351.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1176349741

JSTOR
links.jstor.org

Digital Object Identifier
doi:10.1214/aos/1176349741

Mathematical Reviews number (MathSciNet)
MR811496

Zentralblatt MATH identifier
0582.62061

Subjects
Primary: 62J05: Linear regression
Secondary: 62H25: Factor analysis and principal components; correspondence analysis

Keywords
Errors-in-variables functional maximum likelihood logistic regression measurement error sufficiency

Citation

Stefanski, Leonard A.; Carroll, Raymond J. Covariate Measurement Error in Logistic Regression. Ann. Statist. 13 (1985), no. 4, 1335--1351. doi:10.1214/aos/1176349741. http://projecteuclid.org/euclid.aos/1176349741.


Export citation