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Gonzalo Garcia-Donato

Publications and source records attributed to Gonzalo Garcia-Donato.

5 recordsLinked to original sources
arXiv2017

Handling Factors in Variable Selection Problems

Factors are categorical variables, and the values which these variables assume are called levels. In this paper, we consider the variable selection problem where the set of potential predictors contains both factors and numerical variables. Formally, this problem is a particular case of the standard variable selection problem where factors are coded using dummy variables. As such, the Bayesian solution would be straightforward and, possibly because of this, the problem, despite its importance, has not received much attention in the literature. Nevertheless, we show that this perception is illusory and that in fact several inputs like the assignment of prior probabilities over the model space or the parameterization adopted for factors may have a large (and difficult to anticipate) impact on the results. We provide a solution to these issues that extends the proposals in the standard variable selection problem and does not depend on how the factors are coded using dummy variables. Our approach is illustrated with a real example concerning a childhood obesity study in Spain.

stat.ME↗
arXiv2016

Methods and Tools for Bayesian Variable Selection and Model Averaging in Univariate Linear Regression

In this paper we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available {\tt R}-packages for its practical implementation summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.

stat.CO↗
arXiv2016

BayesVarSel: Bayesian Testing, Variable Selection and model averaging in Linear Models using R

This paper introduces the R package BayesVarSel which implements objective Bayesian methodology for hypothesis testing and variable selection in linear models. The package computes posterior probabilities of the competing hypotheses/models and provides a suite of tools, specifically proposed in the literature, to properly summarize the results. Additionally, \ourpack\ is armed with functions to compute several types of model averaging estimations and predictions with weights given by the posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. The software is intended to appeal to a broad spectrum of users, so the interface has been carefully designed to be highly intuititive and is inspired by the well-known lm function. The issue of prior inputs is carefully addressed. In the default usage (fully automatic for the user)BayesVarSel implements the criteria-based priors proposed by Bayarri et al (2012), but the advanced user has the possibility of using several other popular priors in the literature. The package is available through the Comprehensive R Archive Network, CRAN. We illustrate the use of BayesVarSel with several data examples.

stat.OT↗
arXiv2011

Inferences in Bayesian variable selection problems with large model spaces

An important aspect of Bayesian model selection is how to deal with huge model spaces, since exhaustive enumeration of all the models entertained is unfeasible and inferences have to be based on the very small proportion of models visited. This is the case for the variable selection problem, with a moderate to large number of possible explanatory variables being considered in this paper. We review some of the strategies proposed in the literature and argue that inferences based on empirical frequencies via Markov Chain Monte Carlo sampling of the posterior distribution outperforms recently proposed searching methods. We give a plausible yet very simple explanation of this effect, showing that estimators based on frequencies are unbiased. The results obtained in two illustrative examples provide strong evidence in favor of our arguments.

stat.ME↗