Bayesian Effect Fusion for Categorical Predictors

Pauger, Daniela; Wagner, Helga

doi:10.48550/arxiv.1703.10245

Cited by 1 publication

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“…Another approach to dealing with categorical predictors focuses on modeling and fitting techniques which explicitly consider the special nature of these predictors, with a particular emphasis on regularization-based methods. Tutz and Gertheiss (2016) and Pauger and Wagner (2017) are examples of such approaches, including the references therein. Of particular interest is the removal of a factor or the fusion of levels of a factor.…”

Section: Introductionmentioning

confidence: 99%

Handling Factors in Variable Selection Problems

Garcia-Donato,

Paulo

2017

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%