Name
Abstract | ||
|---|---|---|
A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties. A Bayesian bi-level variable selection method is developed for linear models.A data-augmentation scheme is introduced based on a novel mixture representation.Posterior inference is carried out via a computationally efficient MCMC algorithm.Benchmark results indicate superior performance compared to published methods.Simple extensions are discussed for general models with flexible penalties. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.csda.2017.01.002 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
Bayesian Regularization,Bayesian Variable Selection,Bi-level Variable Selection,Group Bridge,Group Variable Selection,MCMC | Econometrics,Variable-order Bayesian network,Frequentist inference,Feature selection,Bayesian average,Bayes factor,Bayesian linear regression,Bayesian hierarchical modeling,Bayesian statistics,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
110 | C | 0167-9473 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Himel Mallick | 1 | 3 | 2.21 |
| Nengjun Yi | 2 | 20 | 7.67 |