Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data

2021 ◽  
pp. 249-270
Author(s):  
Matthew D. Koslovsky ◽  
Marina Vannucci
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Bayesian Variable Selection ◽  
Multinomial Regression ◽  
Selection For ◽  
Microbiome Data

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.

scholarly journals Bayesian Variable Selection for Pareto Regression Models with Latent Multivariate Log Gamma Process with Applications to Earthquake Magnitudes

Geosciences ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 169 ◽  
Author(s):  
Hou-Cheng Yang ◽  
Guanyu Hu ◽  
Ming-Hui Chen
Keyword(s):  
Variable Selection ◽  
Random Effects ◽  
Regression Models ◽  
The United States ◽  
Bayesian Variable Selection ◽  
United States Geological Survey ◽  
Model Assessment ◽  
Model Framework ◽  
Spatial Random Effects ◽  
Selection For

Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes. In this paper, we propose Bayesian spatial variable selection for Pareto regression based on Bradley et al. and Hu et al. to tackle variable selection issue in generalized linear regression models with spatial random effects. A Bayesian hierarchical latent multivariate log gamma model framework is applied to account for spatial random effects to capture spatial dependence. We use two Bayesian model assessment criteria for variable selection including Conditional Predictive Ordinate (CPO) and Deviance Information Criterion (DIC). Furthermore, we show that these two Bayesian criteria have analytic connections with conditional AIC under the linear mixed model setting. We examine empirical performance of the proposed method via a simulation study and further demonstrate the applicability of the proposed method in an analysis of the earthquake data obtained from the United States Geological Survey (USGS).

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

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 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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