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

DOI10.1016/j.csda.2019.106907OpenAlexW2998409966MaRDI QIDQ2305304

Yanyan Li

Publication date: 10 March 2020

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1805.04602

zbMATH Keywords

incomplete datapublic healthmetropolis-Hastingsobserved likelihood

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Adaptive Bayesian SLOPE: Model Selection With Incomplete Data ⋮ Partial replacement imputation estimation for partially linear models with complex missing pattern covariates ⋮ Estimation of logistic regression with covariates missing separately or simultaneously via multiple imputation methods ⋮ Doubly robust treatment effect estimation with missing attributes

Uses Software

• R
• missMDA
• MICE
• missForest
• misaem

Cites Work

• What is meant by ``missing at random?
• Variable selection and prediction with incomplete high-dimensional data
• Convergence of a stochastic approximation version of the EM algorithm
• The EM Algorithm and Extensions, 2E
• Variable Selection with Incomplete Covariate Data
• Predictive distributions in binary models with missing data
• Inference with Imputed Conditional Means
• Monte Carlo EM for Missing Covariates in Parametric Regression Models
• Adaptive Rejection Sampling for Gibbs Sampling
• Missing covariates in logistic regression, estimation and distribution selection
• The E-MS Algorithm: Model Selection With Incomplete Data
• Missing-Data Methods for Generalized Linear Models
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