Study of Bayesian variable selection method on mixed linear regression models.

ABSTRACT: 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

SUBMITTER: Li Y 

PROVIDER: S-EPMC10022788 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature