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...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2314-4629
2314-4785
DOI:10.1155/2022/3168735