Statistics and Computing

, Volume 28, Issue 3, pp 579–594 | Cite as

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 \(\lambda _T\), for each T, is not a straightforward task. Additionally, little prior information for \(\lambda _T\) 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 \(\lambda _T\). We study the behaviour of each approach using a simulated reference example as well as two real-life examples.


Bayesian model selection Fractional Bayes factor Intrinsic Bayes factor Posterior model probabilities Transformation family selection Variable selection 


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of MathematicsNational Technical University of AthensAthensGreece
  2. 2.Department of StatisticsAthens University of Economics and Business10434Greece

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