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Study of Bayesian variable selection method on mixed linear regression models
Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model prediction accuracy and interpretation effect. redThi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10022788/ https://www.ncbi.nlm.nih.gov/pubmed/36930589 http://dx.doi.org/10.1371/journal.pone.0283100 |
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author | Li, Yong Liu, Hefei Li, Rubing |
author_facet | Li, Yong Liu, Hefei Li, Rubing |
author_sort | Li, Yong |
collection | PubMed |
description | Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model prediction accuracy and interpretation effect. redThis study introduces the Bayesian adaptive group Lasso method to solve the variable selection problem under a mixed linear regression model with a hidden state and explanatory variables with a grouping structure. First, the definition of the implicit state mixed linear regression model is presented. Thereafter, the Bayesian adaptive group Lasso method is used to determine the penalty function and parameters, after which each parameter’s specific form of the fully conditional posterior distribution is calculated. Moreover, the Gibbs algorithm design is outlined. Simulation experiments are conducted to compare the variable selection and parameter estimation effects in different states. Finally, a dataset of Alzheimer’s Disease is used for application analysis. The results demonstrate that the proposed method can identify the observation from different hidden states, but the results of the variable selection in different states are obviously different. |
format | Online Article Text |
id | pubmed-10022788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100227882023-03-18 Study of Bayesian variable selection method on mixed linear regression models Li, Yong Liu, Hefei Li, Rubing PLoS One Research Article Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model prediction accuracy and interpretation effect. redThis study introduces the Bayesian adaptive group Lasso method to solve the variable selection problem under a mixed linear regression model with a hidden state and explanatory variables with a grouping structure. First, the definition of the implicit state mixed linear regression model is presented. Thereafter, the Bayesian adaptive group Lasso method is used to determine the penalty function and parameters, after which each parameter’s specific form of the fully conditional posterior distribution is calculated. Moreover, the Gibbs algorithm design is outlined. Simulation experiments are conducted to compare the variable selection and parameter estimation effects in different states. Finally, a dataset of Alzheimer’s Disease is used for application analysis. The results demonstrate that the proposed method can identify the observation from different hidden states, but the results of the variable selection in different states are obviously different. Public Library of Science 2023-03-17 /pmc/articles/PMC10022788/ /pubmed/36930589 http://dx.doi.org/10.1371/journal.pone.0283100 Text en © 2023 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Yong Liu, Hefei Li, Rubing Study of Bayesian variable selection method on mixed linear regression models |
title | Study of Bayesian variable selection method on mixed linear regression models |
title_full | Study of Bayesian variable selection method on mixed linear regression models |
title_fullStr | Study of Bayesian variable selection method on mixed linear regression models |
title_full_unstemmed | Study of Bayesian variable selection method on mixed linear regression models |
title_short | Study of Bayesian variable selection method on mixed linear regression models |
title_sort | study of bayesian variable selection method on mixed linear regression models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10022788/ https://www.ncbi.nlm.nih.gov/pubmed/36930589 http://dx.doi.org/10.1371/journal.pone.0283100 |
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