A Pseudo‐Bayesian Shrinkage Approach to Regression with Missing Covariates

DOI10.1111/j.1541-0420.2011.01718.xzbMath1272.62101OpenAlexW1963834107WikidataQ51479302 ScholiaQ51479302MaRDI QIDQ4649075

Nanhua Zhang, Roderick J. A. Little

Publication date: 19 November 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2027.42/93673

zbMATH Keywords

Gibbs samplingvariable selectionshrinkagecomplete-case analysisdrop variables analysisnonignorable modeling

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (1)

Performances of Bayesian model selection criteria for generalized linear models with non-ignorably missing covariates

Uses Software

• MICE

Cites Work

• Tobit models: A survey
• Theory and inference for regression models with missing responses and covariates
• Bias correction in logistic regression with missing categorical covariates
• Subsample Ignorable Likelihood for Regression Analysis with Missing Data
• Bayesian methods for generalized linear models with covariates missing at random
• Sieve Maximum Likelihood Estimation for Regression Models With Covariates Missing at Random
• Bayesian Variable Selection in Linear Regression
• Comparing Regressions When Some Predictor Values Are Missing
• Inference and missing data
• Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
• A class of pattern-mixture models for normal incomplete data
• Pattern-Mixture Models for Multivariate Incomplete Data with Covariates
• Monte Carlo EM for Missing Covariates in Parametric Regression Models
• Pattern-Mixture Models for Multivariate Incomplete Data
• Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates
• Missing-Data Methods for Generalized Linear Models

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