Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation

DOI10.1191/1471082X03st056oazbMath1070.62024OpenAlexW2077821776WikidataQ60655836 ScholiaQ60655836MaRDI QIDQ5694518

Anders Skrondal, Sophia Rabe-Hesketh, Andrew R. Pickles

Publication date: 4 October 2005

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1191/1471082x03st056oa

zbMATH Keywords

logistic regressionmeasurement errorfactor modelGLLAMMnonparametric maximum likelihood estimationempirical Bayes prediction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (15)

Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects ⋮ Estimation of the variance matrix in bivariate classical measurement error models ⋮ Simulation-extrapolation with latent heteroskedastic error variance ⋮ A model‐averaging treatment of multiple instruments in Poisson models with errors ⋮ Sequential regression measurement error models with application ⋮ Prediction of Random Effects in Linear and Generalized Linear Models under Model Misspecification ⋮ Improved regression calibration ⋮ Multilevel logistic regression for polytomous data and rankings ⋮ Generalized multilevel structural equation modeling ⋮ A simulation study of statistical tests in logistic measurement error models ⋮ Classical latent variable models for medical research ⋮ The role of conditional likelihoods in latent variable modeling ⋮ Vertex Exchange Method for non-parametric estimation of mixing distributions in logistic mixed models ⋮ A functional method for the conditional logistic regression with errors-in-covariates ⋮ Generalized Linear Latent Variable Models with Flexible Distribution of Latent Variables

Uses Software

• Stata
• GLLAMM

Cites Work

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