Handbook of Bayesian Variable Selection最新文献_第2页

Bayes Factors Based on g-Priors for Variable Selection 基于g先验的贝叶斯因子变量选择

Handbook of Bayesian Variable Selection Pub Date : 2021-12-06 DOI: 10.1201/9781003089018-15

G. García-Donato, M. Steel

引用次数: 1

Bayesian Estimation of Single and Multiple Graphs 单图和多图的贝叶斯估计

Handbook of Bayesian Variable Selection Pub Date : 2021-12-06 DOI: 10.1201/9781003089018-14

Christine B. Peterson, F. Stingo

引用次数: 1

Recent Theoretical Advances with the Discrete Spike-and-Slab Priors 离散峰-板先验的最新理论进展

Handbook of Bayesian Variable Selection Pub Date : 2021-12-06 DOI: 10.1201/9781003089018-2

Shuang Zhou, D. Pati

引用次数: 0

Bayesian Model Averaging in Causal Inference 因果推理中的贝叶斯模型平均

Handbook of Bayesian Variable Selection Pub Date : 2021-12-06 DOI: 10.1201/9781003089018-9

Joseph Antonelli, F. Dominici

引用次数: 1

Bayesian Variable Selection in Spatial Regression Models 空间回归模型中的贝叶斯变量选择

Handbook of Bayesian Variable Selection Pub Date : 2021-12-06 DOI: 10.1201/9781003089018-11

B. Reich, Ana-Maria Staicu

引用次数: 0

Theoretical and Computational Aspects of Continuous Spike-and-Slab Priors 连续钉板先验的理论与计算

Handbook of Bayesian Variable Selection Pub Date : 2021-12-06 DOI: 10.1201/9781003089018-3

N. Narisetty

引用次数: 0

Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO 钉板套索:钉板套索的回顾

Handbook of Bayesian Variable Selection Pub Date : 2020-10-13 DOI: 10.1201/9781003089018-4

Ray Bai, V. Ročková, E. George

{"title":"Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO","authors":"Ray Bai, V. Ročková, E. George","doi":"10.1201/9781003089018-4","DOIUrl":"https://doi.org/10.1201/9781003089018-4","url":null,"abstract":"High-dimensional data sets have become ubiquitous in the past few decades, often with many more covariates than observations. In the frequentist setting, penalized likelihood methods are the most popular approach for variable election and estimation in high-dimensional data. In the Bayesian framework, spike-and-slab methods are commonly used as probabilistic constructs for high-dimensional modeling. Within the context of univariate linear regression, Rockova and George (2018) introduced the spike-and-slab LASSO (SSL), an approach based on a prior which forms a continuum between the penalized likelihood LASSO and the Bayesian point-mass spike-and-slab formulations. Since its inception, the spike-and-slab LASSO has been extended to a variety of contexts, including generalized linear models, factor analysis, graphical models, and nonparametric regression. The goal of this paper is to survey the landscape surrounding spike-and-slab LASSO methodology. First we elucidate the attractive properties and the computational tractability of SSL priors in high dimensions. We then review methodological developments of the SSL and outline several theoretical developments. We illustrate the methodology on both simulated and real datasets.","PeriodicalId":403293,"journal":{"name":"Handbook of Bayesian Variable Selection","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132450842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23