G. García-Donato, R. Paulo
Factors are categorical variables; the values which these variables assume are called levels. We consider the variable selection problem where the set of potential predictors contains both factors and numerical variables. This problem is a particular case of the standard variable selection problem where factors are coded using dummy variables. The Bayesian solution should then be straightforward and, possibly because of this, the problem, despite its importance, has not received much attention in the literature. We show that this perception is illusory and that several inputs like the assignment of prior probabilities over the model space or the parameterization adopted for factors may have 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.
Palabras clave / Keywords: Bayes factor, categorical variables, generalized inverse, multiplicity, prior distributions
Programado
Sesión GT08-1a Inferencia Bayesiana-1 (Parte 1): New Insights on the Role of the Bayesian Thinking in Model Selection Problems (BAYES-1a). Organizador: Gonzalo García-Donato
29 de mayo de 2018 10:50
Sala 3
