A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization

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dc.creatorEyheramendy, Susana
dc.creatorMadigan, David
dc.date2007-08-07
dc.date.accessioned2026-07-07T08:24:35Z
dc.date.available2026-07-07T08:24:35Z
dc.descriptionWe present a class of sparse generalized linear models that include probit and logistic regression as special cases and offer some extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. We apply our method in text classification and in simulated data and show that our method outperforms the logistic and probit models and also the elastic net, in general by a substantial margin.
dc.descriptionPublished at http://dx.doi.org/10.1214/074921707000000067 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0708.0959
dc.identifierhttp://arxiv.org/abs/0708.0959
dc.identifierIMS Lecture Notes Monograph Series 2007, Vol. 54, 76-91
dc.identifierdoi:10.1214/074921707000000067
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/136384
dc.subjectMethodology
dc.subject62-02, 62J12 (Primary)
dc.titleA flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization
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