A New Method for Dealing with Measurement Error in Explanatory Variables of Regression Models

DOI10.1111/j.0006-341X.2004.00164.xzbMath1109.62062WikidataQ52001064 ScholiaQ52001064MaRDI QIDQ3435678

Laurence S. Freedman, Douglas Midthune, Vitaly Fainberg, Victor Kipnis, Raymond J. Carroll

Publication date: 7 May 2007

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

logistic regressionmeasurement errorerrors-in-variablescase-control studyclassification treesregression calibrationlinear discriminant analysiscohort study

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12)

Related Items (9)

Moment adjusted imputation for multivariate measurement error data with applications to logistic regression ⋮ A Moment-Adjusted Imputation Method for Measurement Error Models ⋮ Multistage Evaluation of Measurement Error in a Reliability Study ⋮ Approximate maximum likelihood estimation for logistic regression with covariate measurement error ⋮ Sequential estimation in generalized linear models when covariates are subject to errors ⋮ Assessing the impact of measurement error in modeling change in the absence of auxiliary data ⋮ Semiparametric methods for survival data with measurement error under additive hazards cure rate models ⋮ Log-multiplicative association models as item response models ⋮ On the use of repeated measurement errors in linear regression models

Cites Work

• Locally Ancillary Quasi-Score Models for Errors-in-Covariates
• Consistent Functional Methods for Logistic Regression With Errors in Covariates
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