Bayesian variable selection using the hyper-g prior in WinBUGS

2018 ◽  
Vol 10 (6) ◽  
pp. e1442
Author(s):  
Konstantinos Perrakis ◽  
Ioannis Ntzoufras
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
G Prior

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Limited Information ◽  
Online Application ◽  
Stochastic Search Variable Selection ◽  
Selection Approach ◽  
Pain Ratings ◽  
Research Questions ◽  
Standard Techniques ◽  
Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

scholarly journals Correction to: MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Matthew D. Koslovsky ◽  
Marina Vannucci
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
R Package ◽  
Bayesian Variable Selection ◽  
Multinomial Regression

An amendment to this paper has been published and can be accessed via the original article.

Scalable Bayesian variable selection regression models for count data

2020 ◽  
pp. 187-219 ◽  
Author(s):  
Yinsen Miao ◽  
Jeong Hwan Kook ◽  
Yadong Lu ◽  
Michele Guindani ◽  
Marina Vannucci
Keyword(s):  
Variable Selection ◽  
Count Data ◽  
Regression Models ◽  
Bayesian Variable Selection

scholarly journals Identifying Activity-sensitive Spectral Lines: A Bayesian Variable Selection Approach

2019 ◽  
Vol 158 (5) ◽  
pp. 210
Author(s):  
Bo Ning ◽  
Alexander Wise ◽  
Jessi Cisewski-Kehe ◽  
Sarah Dodson-Robinson ◽  
Debra Fischer
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Spectral Lines ◽  
Selection Approach

scholarly journals Bayesian Variable Selection Regression of Multivariate Responses for Group Data

2017 ◽  
Vol 12 (4) ◽  
pp. 1039-1067 ◽  
Author(s):  
B. Liquet ◽  
K. Mengersen ◽  
A. N. Pettitt ◽  
M. Sutton
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Group Data ◽  
Multivariate Responses
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