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Model selection in sparse contingency tables: LASSO penalties vs classical method

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dc.contributor.author Conde, Susana
dc.contributor.author MacKenzie, Gilbert
dc.date.accessioned 2013-01-10T12:05:40Z
dc.date.available 2013-01-10T12:05:40Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10344/2802
dc.description peer-reviewed en_US
dc.description.abstract We compare improved classical backward elimination and forward selection methods of model selection in sparse contingency tables with methods based on a regularisation approach involving the least absolute shrinkage and selection operator (LASSO) and the Smooth LASSO. The results show that the modified classical methods outperform the regularisation methods, by producing sparser models which are always hierarchical. Curiously, models selected by the regularisation methods often include effects which are known to be inestimable in the classical paradigm. Our findings support the use of classical methodology. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 27th International Workshop on Statistical Modelling;
dc.relation.uri http://www.statmod.org/workshops.htm
dc.subject contingency tables en_US
dc.subject model selection en_US
dc.subject regularisation en_US
dc.subject smooth LASSO en_US
dc.subject sparseness en_US
dc.title Model selection in sparse contingency tables: LASSO penalties vs classical method 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 SFI
dc.relation.projectid 07/MI012
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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