Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

Fouskakis, D

dc.contributor.author	Fouskakis, D	en
dc.date.accessioned	2014-03-01T02:07:57Z
dc.date.available	2014-03-01T02:07:57Z
dc.date.issued	2012	en
dc.identifier.issn	03772217	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29624
dc.subject	Bayesian variable selection	en
dc.subject	Genetic algorithm	en
dc.subject	Laplace approximation	en
dc.subject	Simulated annealing	en
dc.subject	Stochastic optimization	en
dc.subject	Tabu search	en
dc.subject.other	Bayesian variable selection	en
dc.subject.other	Computational costs	en
dc.subject.other	Criterion functions	en
dc.subject.other	Generalized linear model	en
dc.subject.other	Laplace approximation	en
dc.subject.other	Logistic regression models	en
dc.subject.other	Model composition	en
dc.subject.other	Model probabilities	en
dc.subject.other	Model search	en
dc.subject.other	Stochastic optimization algorithm	en
dc.subject.other	Stochastic optimization methods	en
dc.subject.other	Stochastic optimizations	en
dc.subject.other	Approximation algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Laplace transforms	en
dc.subject.other	Logistics	en
dc.subject.other	Regression analysis	en
dc.subject.other	Simulated annealing	en
dc.subject.other	Stochastic models	en
dc.subject.other	Stochastic systems	en
dc.subject.other	Tabu search	en
dc.subject.other	Genetic algorithms	en
dc.title	Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ejor.2012.01.040	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ejor.2012.01.040	en
heal.publicationDate	2012	en
heal.abstract	In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with ""vanilla"" versions of simulated annealing, genetic algorithm and tabu search. © 2012 Elsevier B.V. All rights reserved.	en
heal.journalName	European Journal of Operational Research	en
dc.identifier.doi	10.1016/j.ejor.2012.01.040	en
dc.identifier.volume	220	en
dc.identifier.issue	2	en
dc.identifier.spage	414	en
dc.identifier.epage	422	en