Objective Bayesian transformation and variable selection using default Bayes factors

In this work, the problem of transformation and simultaneous variable selection is thoroughly treated via objective Bayesian approaches by the use of default Bayes factor variants. Four uniparametric families of transformations (Box-Cox, Modulus, Yeo-Johnson and Dual), denoted by T, are evaluated and compared. The subjective prior elicitation for the transformation parameter , for each T, is not a straightforward task. Additionally, little prior information for is expected to be available, and therefore, an objective method is required. The intrinsic Bayes factors and the fractional Bayes factors allow us to incorporate default improper priors for . We study the behaviour of each approach using a simulated reference example as well as two real-life examples.