Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2285
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dc.contributor.authorBRIJS, Tom-
dc.contributor.authorKARLIS, Dimitris-
dc.contributor.authorSWINNEN, Gilbert-
dc.contributor.authorVANHOOF, Koen-
dc.contributor.authorWETS, Geert-
dc.contributor.authorManchanda, P.-
dc.date.accessioned2007-11-13T14:26:39Z-
dc.date.available2007-11-13T14:26:39Z-
dc.date.issued2004-
dc.identifier.citationSTATISTICA NEERLANDICA, 58(3). p. 322-348-
dc.identifier.issn0039-0402-
dc.identifier.urihttp://hdl.handle.net/1942/2285-
dc.description.abstractThis paper describes a multivariate Poisson mixture model for clustering supermarket shoppers based on their purchase frequency in a set of product categories. The multivariate nature of the model accounts for cross-selling effects between the purchases made in different product categories. However, for computational reasons, most multivariate approaches limit the covariance structure by including just one common interaction term, or by not including any covariance at all. Although this reduces the number of parameters significantly, it is often too simplistic as typically multiple interactions exist on different levels. This paper proposes a theoretically more complete variance/covariance structure of the multivariate Poisson model, based on domain knowledge or preliminary statistical analysis of significant purchase interaction effects in the data. Consequently, the model does not contain more parameters than necessary, whilst still accounting for the existing covariance in the data. Practically, retail category managers can use the model to devise customized merchandising strategies.-
dc.language.isoen-
dc.publisherBLACKWELL PUBL LTD-
dc.subject.othermixture models; clustering; EM algorithm; multivariate Poisson; product purchasing-
dc.titleA multivariate Poisson mixture model for marketing applications-
dc.typeJournal Contribution-
dc.identifier.epage348-
dc.identifier.issue3-
dc.identifier.spage322-
dc.identifier.volume58-
local.format.pages27-
local.bibliographicCitation.jcatA1-
dc.description.notesLimburgs Univ Ctr, Dept Econ, B-3590 Diepenbeek, Belgium. Athens Univ Econ, Dept Stat, Athens 10434, Greece. Univ Chicago, Grad Sch Business, Chicago, IL 60637 USA.Brijs, T, Limburgs Univ Ctr, Dept Econ, Univ Campus, B-3590 Diepenbeek, Belgium.tom.brijs@luc.ac.be karlis@aueb.gr puneet.manchanda@gsb.uchicago.edu-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1111/j.1467-9574.2004.00125.x-
dc.identifier.isi000223056200005-
item.fullcitationBRIJS, Tom; KARLIS, Dimitris; SWINNEN, Gilbert; VANHOOF, Koen; WETS, Geert & Manchanda, P. (2004) A multivariate Poisson mixture model for marketing applications. In: STATISTICA NEERLANDICA, 58(3). p. 322-348.-
item.fulltextNo Fulltext-
item.accessRightsClosed Access-
item.validationecoom 2005-
item.contributorBRIJS, Tom-
item.contributorWETS, Geert-
item.contributorSWINNEN, Gilbert-
item.contributorKARLIS, Dimitris-
item.contributorVANHOOF, Koen-
item.contributorManchanda, P.-
crisitem.journal.issn0039-0402-
crisitem.journal.eissn1467-9574-
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