Imputation and Variable Selection in Linear Regression Models with Missing Covariates

DOI10.1111/J.1541-0420.2005.00317.XzbMath1077.62053OpenAlexW2051458787WikidataQ44641375 ScholiaQ44641375MaRDI QIDQ5714638

Thomas R. Belin, W. John Boscardin, Xiaowei Yang

Publication date: 3 January 2006

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2005.00317.x

zbMATH Keywords

MCMCBayesian variable selectionMultiple imputationModel averaging

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items (8)

Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods ⋮ Bayesian spatial modeling of incomplete data with application to HIV prevalence in Ghana ⋮ Penalized estimating equations for generalized linear models with multiple imputation ⋮ Robust methods for generalized linear models with nonignorable missing covariates ⋮ Order selection tests with multiply imputed data ⋮ Estimation in zero-inflated binomial regression with missing covariates ⋮ Missing covariates in logistic regression, estimation and distribution selection ⋮ Sure independence screening in the presence of missing data

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