Statistics and Computing

, Volume 23, Issue 4, pp 491–499

Computation for intrinsic variable selection in normal regression models via expected-posterior prior


DOI: 10.1007/s11222-012-9325-9

Cite this article as:
Fouskakis, D. & Ntzoufras, I. Stat Comput (2013) 23: 491. doi:10.1007/s11222-012-9325-9


In this paper, we focus on the variable selection problem in normal regression models using the expected-posterior prior methodology. We provide a straightforward MCMC scheme for the derivation of the posterior distribution, as well as Monte Carlo estimates for the computation of the marginal likelihood and posterior model probabilities. Additionally, for large spaces, a model search algorithm based on \(\mathit{MC}^{3}\) is constructed. The proposed methodology is applied in two real life examples, already used in the relevant literature of objective variable selection. In both examples, uncertainty over different training samples is taken into consideration.


Bayesian variable selection Expected-posterior priors Imaginary data Intrinsic priors Jeffreys prior Objective model selection methods Normal regression models 

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

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

Personalised recommendations