Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses

Yuanying Zhao, Xingde Duan and Niansheng Tang

Journal of Mathematics, 2022, vol. 2022, 1-12

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:3168735

DOI: 10.1155/2022/3168735

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