Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses

The main purpose of this article is to develop a Bayesian adaptive lasso procedure for analyzing linear regression models with nonignorable missing responses, in which the missingness mechanism is specified by a logistic regression model. A sampling procedure combining the Gibbs sampler and Metropol...

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Published in:Journal of mathematics (Hidawi) 2022, Vol.2022 (1)
Main Authors: Zhao, Yuanying, Duan, Xingde
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Feature selection
Goodness of fit
Mathematics
Parameter estimation
Regression analysis
Regression coefficients
Regression models
Statistical analysis
Variables
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description The main purpose of this article is to develop a Bayesian adaptive lasso procedure for analyzing linear regression models with nonignorable missing responses, in which the missingness mechanism is specified by a logistic regression model. A sampling procedure combining the Gibbs sampler and Metropolis-Hastings algorithm is employed to obtain the Bayesian estimates of the regression coefficients, shrinkage coefficients, missingness mechanism models parameters, and their standard errors. We extend the partial posterior predictive p value for goodness-of-fit statistic to investigate the plausibility of the posited model. Finally, several simulation studies and the air pollution data example are undertaken to demonstrate the newly developed methodologies.
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subjects Algorithms
Bayesian analysis
Feature selection
Goodness of fit
Mathematics
Parameter estimation
Regression analysis
Regression coefficients
Regression models
Statistical analysis
Variables
title Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses
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