Bayesian Variable Selection

2020 ◽  
pp. 121-135
Author(s):  
Matthew Sutton
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection

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

Bayesian variable selection for Poisson regression with underreported responses

2010 ◽  
Vol 54 (12) ◽  
pp. 3289-3299 ◽  
Author(s):  
Stephanie Powers ◽  
Richard Gerlach ◽  
James Stamey
Keyword(s):  
Variable Selection ◽  
Poisson Regression ◽  
Bayesian Variable Selection ◽  
Selection For

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

scholarly journals Comparing Spike and Slab Priors for Bayesian Variable Selection

2016 ◽  
Vol 40 (4) ◽  
Author(s):  
Gertraud Malsiner-Walli ◽  
Helga Wagner
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Psychiatric Patients ◽  
Simulated Data ◽  
Bayesian Variable Selection ◽  
Data Set ◽  
Final Model ◽  
Mcmc Sampling ◽  
Regression Effect ◽  
Inclusion Probabilities

An important task in building regression models is to decide which regressors should be included in the final model. In a Bayesian approach, variable selection can be performed using mixture priors with a spike and a slab component for the effects subject to selection. As the spike is concentrated at zero, variable selection is based on the probability of assigning the corresponding regression effect to the slab component. These posterior inclusion probabilities can be determined by MCMC sampling. In this paper we compare the MCMC implementations for several spike and slab priors with regard to posterior inclusion probabilities and their sampling efficiency for simulated data. Further, we investigate posterior inclusion probabilities analytically for different slabs in two simple settings. Application of variable selection with spike and slab priors is illustrated on a data set of psychiatric patients where the goal is to identify covariates affecting metabolism.

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