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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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/3168735 |
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| _version_ | 1849469423164850176 |
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| author | Yuanying Zhao Xingde Duan |
| author_facet | Yuanying Zhao Xingde Duan |
| author_sort | Yuanying Zhao |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-f5a0f1a1f5f8440e8e3f6e7ac194d58c |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-f5a0f1a1f5f8440e8e3f6e7ac194d58c2025-08-20T03:25:29ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/3168735Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing ResponsesYuanying Zhao0Xingde Duan1College of Mathematics and Information ScienceSchool of Mathematics and StatisticsThe 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.http://dx.doi.org/10.1155/2022/3168735 |
| spellingShingle | Yuanying Zhao Xingde Duan Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses Journal of Mathematics |
| title | Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses |
| title_full | Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses |
| title_fullStr | Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses |
| title_full_unstemmed | Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses |
| title_short | Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses |
| title_sort | bayesian adaptive lasso for regression models with nonignorable missing responses |
| url | http://dx.doi.org/10.1155/2022/3168735 |
| work_keys_str_mv | AT yuanyingzhao bayesianadaptivelassoforregressionmodelswithnonignorablemissingresponses AT xingdeduan bayesianadaptivelassoforregressionmodelswithnonignorablemissingresponses |