Missing covariates in logistic regression, estimation and distribution selection

DOI10.1177/1471082X1001100204MaRDI QIDQ4970600

Fabrizio Consentino, Gerda Claeskens

Publication date: 7 October 2020

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

zbMATH Keywords

model selectionAkaike information criterionmultiple imputationmissing covariates\(t\)-distribution

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework

Cites Work

• An Akaike information criterion for model selection in the presence of incomplete data.
• Missing data imputation using the multivariate \(t\) distribution
• Order selection tests with multiply imputed data
• Logistic regression with a partially observed covariate
• Variable Selection with Incomplete Covariate Data
• Performing likelihood ratio tests with multiply-imputed data sets
• Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
• Inference Using Conditional Logistic Regression with Missing Covariates
• Monte Carlo EM for Missing Covariates in Parametric Regression Models
• Multivariate T-Distributions and Their Applications
• Adaptive Rejection Sampling for Gibbs Sampling
• Imputation and Variable Selection in Linear Regression Models with Missing Covariates
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