Covariate Measurement Error in Logistic Regression.

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. This document introduces 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. Additional keywords: mathematical models.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1985
Accession Number
ADA160277

Entities

People

  • L. A. Stefanski
  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Asymptotic Normality
  • Asymptotic Series
  • Cardiovascular Physiological Phenomena
  • Computational Science
  • Data Science
  • Information Science
  • Mathematical Models
  • Measurement
  • Models
  • Normal Distribution
  • North Carolina
  • Scientific Research
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.