Handbook of Bayesian variable selection
著者
書誌事項
Handbook of Bayesian variable selection
（Handbooks of modern statistical methods / Series editors, Garrett Fitzmaurice）
CRC Press, 2022
- : hbk
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注記
"A Chapman & Hall book."
Includes bibliographical references (p. 457-459) and index
内容説明・目次
内容説明
* Provides a comprehensive review of methods and applications of Bayesian variable selection.
* Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection.
* Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement.
* Includes contributions by experts in the field.
目次
1. Discrete Spike-and-Slab Priors: Models and Computational Aspects
2. Recent Theoretical Advances with the Discrete Spike-and-Slab Priors
3. Theoretical and Computational Aspects of Continuous Spike-and-Slab Priors
4. Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
5. Adaptive Computational Methods for Bayesian Variable Selection
6. Theoretical guarantees for the horseshoe and other global-local shrinkage priors
7. MCMC for Global-Local Shrinkage Priors in High-Dimensional Settings
8. Variable Selection with Shrinkage Priors via Sparse Posterior Summaries
9. Bayesian Model Averaging in Causal Inference
10. Variable Selection for Hierarchically-Related Outcomes: Models and Algorithms
11. Bayesian variable selection in spatial regression models
12. Effect Selection and Regularization in Structured Additive Distributional Regression
13. Sparse Bayesian State-Space and Time-Varying Parameter Models
14. Bayesian estimation of single and multiple graphs
15. Bayes Factors Based on g-Priors for Variable Selection
16. Balancing Sparsity and Power: Likelihoods, Priors, and Misspecification
17. Variable Selection and Interaction Detection with Bayesian Additive Regression Trees
18. Variable Selection for Bayesian Decision Tree Ensembles
19. Stochastic Partitioning for Variable Selection in Multivariate Mixture of Regression Models
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