1st
Edition
Handbook
of Bayesian Variable Selection
Edited
by Mahlet G. Tadesse and Marina Vannucci
Copyright
Year 2022
ISBN 9780367543761
Published December 20, 2021 by Chapman and Hall/CRC
490 Pages 91 B/W
Illustrations
Available
on Taylor & Francis eBooks
Book Description
Bayesian variable selection has experienced substantial
developments over the past 30 years with the proliferation of large data sets.
Identifying relevant variables to include in a model allows simpler
interpretation, avoids overfitting and multicollinearity, and can provide
insights into the mechanisms underlying an observed phenomenon. Variable
selection is especially important when the number of potential predictors is
substantially larger than the sample size and sparsity can reasonably be
assumed.
The Handbook of Bayesian Variable Selection provides
a comprehensive review of theoretical, methodological and computational aspects
of Bayesian methods for variable selection. The topics covered include
spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian
model averaging, partitioning methods, as well as variable selection in
decision trees and edge selection in graphical models. The handbook targets
graduate students and established researchers who seek to understand the latest
developments in the field. It also provides a valuable reference for all
interested in applying existing methods and/or pursuing methodological
extensions.
Features:
• 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.
Table of Contents with Supporting
Material and Errata Corrige
Part I: Spike-and-Slab
Priors – Theoretical and Computational Aspects
1. Discrete Spike-and-Slab Priors:
Models and Computational Aspects (Vannucci)
o
Note: the printed version of the chapter contains some incorrect references
o
Slides from invited talk at ISBA 2022
2. Recent Theoretical Advances with
the Discrete Spike-and-Slab Priors (Zhou & Pati)
3. Theoretical and Computational
Aspects of Continuous Spike-and-Slab Priors (Narisetty)
4. Spike-and-Slab Meets LASSO: A
Review of the Spike-and-Slab LASSO (Bai, Rockova
& George)
5. Adaptive Computational Methods for
Bayesian Variable Selection (Griffin, Latuszynski and
Steel)
Part II: Continuous Shrinkage
Priors – Theoretical and Computational Aspects
6. Theoretical Guarantees for the
Horseshoe and Other Global-Local Shrinkage Priors (Stephanie van der Pas)
7. MCMC for Global-Local Shrinkage
Priors in High-Dimensional Settings (Bhattacharya and Johndrow)
8. Variable Selection with Shrinkage
Priors via Sparse Posterior Summaries (Zhang, Yu and Bondell)
Part III: Extensions to various
Modeling Frameworks
9. Bayesian Model Averaging in Causal
Inference (Antonelli and Dominici)
10. Variable Selection for
Hierarchically-Related Outcomes: Models and Algorithms (Ruffieux,
Bottolo and Richardson)
11. Bayesian Variable Selection in
Spatial Regression Models (Reich and Staicu)
12. Effect Selection and
Regularization in Structured Additive Distributional Regression (Wiemann, Kneib and Wagner)
13.
Sparse Bayesian State-Space and Time-Varying Parameter Models
(Frühwirth-Schnatter
and Knaus)
14. Bayesian Estimation of Single and
Multiple Graphs (Peterson and Stingo)
Part IV: Other Approaches to
Bayesian Variable Selection
15. Bayes Factors Based on g-Priors
for Variable Selection (Garcia-Donato and Steel)
16. Balancing Sparsity and Power:
Likelihoods, Priors, and Misspecification (Rossell
and Rubio)
17. Variable Selection and Interaction
Detection with Bayesian Additive Regression Trees (Carvalho, George, Hahn and
McCulloch)
18. Variable Selection for Bayesian
Decision Tree Ensembles (Linero and Du)
19. Stochastic Partitioning for
Variable Selection in Multivariate Mixture of Regression Models (Monni and Tadesse)
Mahlet Tadesse is Professor and Chair in the
Department of Mathematics and Statistics at Georgetown University, USA. Her
research over the past two decades has focused on Bayesian modeling for
high-dimensional data with an emphasis on variable selection methods and
mixture models. She also works on various interdisciplinary projects in
genomics and public health. She is a recipient of the Myrto
Lefkopoulou Distinguished Lectureship award, an
elected member of the International Statistical Institute and an elected fellow
of the American Statistical Association.
Marina Vannucci is Noah Harding Professor of
Statistics at Rice University, USA. Her research over the past 25 years has
focused on the development of methodologies for Bayesian variable selection in
linear settings, mixture models and graphical models, and on related
computational algorithms. She also has a solid history of scientific
collaborations and is particularly interested in applications of Bayesian
inference to genomics and neuroscience. She has received an NSF CAREER award
and the Mitchell prize by ISBA for her research, and the Zellner Medal by ISBA
for exceptional service over an extended period of time with long-lasting
impact. She is an elected Member of ISI and RSS and an elected fellow of ASA,
IMS, AAAS and ISBA.