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LASSO penalised likelihood in high-dimensional contingency tables

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dc.contributor.author Conde, Susana
dc.contributor.author MacKenzie, Gilbert
dc.date.accessioned 2013-01-10T11:24:39Z
dc.date.available 2013-01-10T11:24:39Z
dc.date.issued 2011
dc.identifier.uri http://hdl.handle.net/10344/2800
dc.description peer-reviewed en_US
dc.description.abstract We consider several least absolute shrinkage and selection operator (LASSO) penalized likelihood approaches in high dimensional contingency tables and with hierarchical log-linear models. These include the proposal of a parametric, analytic, convex, approximation to the LASSO. We compare them with "classical" stepwise search algorithms. The results show that both backwards elimination and forward selection algorithms select more parsimonious (i.e. sparser) models which are always hierarchical, unlike the competing LASSO techniques. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 26th International Workshop on Statistical Modelling;
dc.relation.uri http://www.statmod.org/workshops.htm
dc.subject LASSO en_US
dc.subject model selection en_US
dc.subject penalized likelihood en_US
dc.subject stepwise search algorithms en_US
dc.title LASSO penalised likelihood in high-dimensional contingency tables en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor Glaxosmithkline (GSK) en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 07/MI/012 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1405361


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