Methods for missing covariates in logistic regression

DOI10.1080/03610910008813598zbMath0968.62529OpenAlexW2000378434MaRDI QIDQ4490143

Myunghee Cho Paik

Publication date: 10 July 2000

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910008813598

zbMATH Keywords

efficiencylogistic regressionimputationnonparametric maximum likelihoodmissing covariate

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Generalized linear models (logistic models) (62J12)

Related Items (1)

APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001

Cites Work

• Survey Nonresponse Adjustments for Estimates of Means
• Inference and missing data
• Analysis of Longitudinal Binary Data from Multiphase Sampling
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• The Generalized Estimating Equation Approach When Data Are Not Missing Completely at Random
• Multiple Imputation Methods for the Missing Covariates in Generalized Estimating Equation
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• A mean score method for missing and auxiliary covariate data in regression models
• Semiparametric mixtures in case-control studies

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