Standard assumptions incorporated into Bayesian model selection procedures result in procedures that are not competitive with commonly used penalized likelihood methods. We propose modifications of these methods by imposing nonlocal prior densities on model parameters. We show that the resulting model selection procedures are consistent in linear model settings when the number of possible covar...

in this paper we propose a simple non-parametric model for multiple crite-ria supplier selection problem. the proposed model does not generate a zeroweight for a certain criterion and ranks the suppliers without solving the modeln times (one linear programming (lp) for each supplier) and therefore allowsthe manager to get faster results. the methodology is illustrated using anexample.

In the problem of selecting a linear model to approximate the true unknown regression model, some necessary and/or sufficient conditions are established for the asymptotic validity of various model selection procedures such as Akaike’s AIC, Mallows’ Cp, Shibata’s FPEλ, Schwarz’ BIC, generalized AIC, crossvalidation, and generalized cross-validation. It is found that these selection procedures c...

Technology assessment and selection has a substantial impact on organizations procedures in regards to technology transfer. Technological decisions are usually made by a group of experts, and whereby integrity of these viewpoints to a single decision can be quite complex. Today, operational databases and data warehouses exist to manage and organize data with specific features and henceforth, th...

Disputes about econometric methodology partly reflect a lack of evidence on alternative approaches. We reconsider econometric model selection from a computer-automation perspective, focusing on general-to-specific reductions, embodied in PcGets. Starting from a general congruent model, standard testing procedures eliminate statistically-insignificant variables, with diagnostic tests checking th...

Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distingui...

In this paper we present a fully coherent and consistent objective Bayesian analysis of the linear regression model using intrinsic priors. The intrinsic prior is a scaled mixture of g-priors and promotes shrinkage towards the subspace defined by a base (or null) model. While it has been established that the intrinsic prior provides consistent model selectors across a range of models, the poste...