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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
mathematics
Regularization in regression: Comparing Bayesian and frequentist methods in a poorly informative situation
Bayesian Analysis, Volume 7, No. 2, Year 2012
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Description
Using a collection of simulated and real benchmarks, we compare Bayesian and frequentist regularization approaches under a low informative constraint when the number of variables is almost equal to the number of observations on simulated and real datasets. This comparison includes new global noninformative approaches for Bayesian variable selection built on Zellner's g-priors that are similar to Liang et al. (2008). The interest of those calibration-free proposals is discussed. The numerical experiments we present highlight the appeal of Bayesian regularization methods, when compared with non-Bayesian alternatives. They dominate frequentist methods in the sense that they provide smaller prediction errors while selecting the most relevant variables in a parsimonious way. © 2012 International Society for Bayesian Analysis.
Authors & Co-Authors
Celeux, Gilles
France, Palaiseau
Inria Saclay
El-Anbari, Mohammed
Morocco, Marakech
Université Cadi Ayyad
Marin, Jean Michel
France, Montpellier
Université de Montpellier
Robert, Christian P.
France, Paris
Université Paris-dauphine
Statistics
Citations: 51
Authors: 4
Affiliations: 4
Identifiers
Doi:
10.1214/12-BA716
ISSN:
19360975