Sieve Maximum Likelihood Estimation for Regression Models With Covariates Missing at Random

DOI10.1198/016214507000001058zbMath1332.62112OpenAlexW2073792557MaRDI QIDQ3632596

Donglin Zeng, Qingxia Chen, Joseph G. Ibrahim

Publication date: 12 June 2009

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/016214507000001058

zbMATH Keywords

B-splinesemiparametric efficiencygeneralized linear modelmodel misspecificationmissing covariates

Mathematics Subject Classification ID

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

Related Items (8)

Estimation in semiparametric models with missing data ⋮ Missing data methods in longitudinal studies: a review ⋮ Model selection of generalized partially linear models with missing covariates ⋮ Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression ⋮ A Pseudo‐Bayesian Shrinkage Approach to Regression with Missing Covariates ⋮ Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information ⋮ Nonparametric regression with selectively missing covariates ⋮ Subsample Ignorable Likelihood for Regression Analysis with Missing Data

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