Abstract
Selecting a subset of variables for linear models remains an active area of research. This article reviews many of the recent contributions to the Bayesian model selection and shrinkage prior literature. A posterior variable selection summary is proposed, which distills a full posterior distribution over regression coefficients into a sequence of sparse linear predictors.
Original language | English (US) |
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Pages (from-to) | 435-448 |
Number of pages | 14 |
Journal | Journal of the American Statistical Association |
Volume | 110 |
Issue number | 509 |
DOIs | |
State | Published - Jan 2 2015 |
Externally published | Yes |
Keywords
- Decision theory
- Linear regression
- Loss function
- Model selection
- Parsimony
- Shrinkage prior
- Sparsity
- Variable selection
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty