A novel variational Bayesian method for variable selection in logistic regression models
type: Item
instance of: scholarly article
title: A novel variational Bayesian method for variable selection in logistic regression models
author: Chunxia Zhang
author: Shuang Xu
author: Jiang-She Zhang
published in: Computational Statistics and Data Analysis
publication date: 2019-02-21T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62J12
zbMATH DE Number: 7027241
zbMATH Keywords: variable selection
zbMATH Keywords: logistic regression
zbMATH Keywords: sparse model
zbMATH Keywords: variational Bayes
zbMATH Keywords: indicator model
zbMATH Keywords: high-dimensional data
describes a project that uses: icmm
describes a project that uses: spinyReg
describes a project that uses: BVSNLP
describes a project that uses: PRMLT
describes a project that uses: aplore3
describes a project that uses: EMVS
describes a project that uses: BayesLogit
MaRDI profile type: Publication
full work available at URL: j.csda.2018.08.025
OpenAlex ID: W2890748370
cites work: Q5483032
cites work: Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
cites work: Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies
cites work: Needles and straw in a haystack: posterior concentration for possibly sparse sequences
cites work: Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
cites work: On the use of Cauchy prior distributions for Bayesian logistic regression
cites work: Expectation propagation in linear regression models with spike-and-slab priors
cites work: Applied Logistic Regression
cites work: Paths Following Algorithm for Penalized Logistic Regression Using SCAD and MCP
cites work: Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates
cites work: Variable selection for regression models
cites work: Penalized regression, standard errors, and Bayesian Lassos
cites work: Combining a relaxed EM algorithm with Occam's razor for Bayesian variable selection in high-dimensional regression
cites work: A review of Bayesian variable selection methods: what, how and which
cites work: A variational Bayes approach to variable selection
cites work: The Bayesian Lasso
cites work: Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
cites work: Selecting massive variables using an iterated conditional modes/medians algorithm
cites work: Tractable Bayesian Variable Selection: Beyond Normality
cites work: Particle EM for Variable Selection
cites work: EMVS: The EM Approach to Bayesian Variable Selection
cites work: Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
cites work: Sequential updating of conditional probabilities on directed graphical structures
cites work: Efficient methods for estimating constrained parameters with applications to regularized (Lasso) logistic regression
cites work: Q4864293
cites work: Q5406026
cites work: On variational Bayes estimation and variational information criteria for linear regression models
cites work: Nearly unbiased variable selection under minimax concave penalty
Wikidata QID: Q129240989
DOI: 10.1016/J.CSDA.2018.08.025
description: scientific article; zbMATH DE number 7027241