Skip navigation
Logo
User training | Reference and search service

Library catalog

Retrievo
EDS
b-on
More
resources
Content aggregators
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/10071/13138
Full metadata record
acessibilidade
DC FieldValueLanguage
dc.contributor.authorTrindade, G.-
dc.contributor.authorDias, J. G.-
dc.contributor.authorAmbrósio, J.-
dc.date.accessioned2017-04-26T10:51:28Z-
dc.date.available2017-04-26T10:51:28Z-
dc.date.issued2017-
dc.identifier.issn0096-3003-
dc.identifier.urihttp://hdl.handle.net/10071/13138-
dc.description.abstractThis paper introduces a new application of the Sequential Quadratic Programing (SQP) algorithm to the context of clustering aggregate panel data. The optimization applies the SQP method in parameter estimation. The method is illustrated on synthetic and empirical data sets. Distinct models are estimated and compared with varying numbers of clusters, explanatory variables, and data aggregation. Results show a good performance of the SQP algorithm for synthetic and empirical data sets. Synthetic data sets were simulated assuming two segments and two covariates, and the correlation between the two covariates was controlled in three scenarios: rho = 0.00 (no correlation), rho = 0.25 (weak correlation), and rho = 0.50 (moderate correlation). The SQP algorithm identifies the correct number of segments for these three scenarios based on all information criteria (AIC, AIC3, and BIC) and retrieves the unobserved heterogeneity in preferences. The empirical case study applies the SQP algorithm to consumer purchase data to find market segments. Results for the empirical data set can provide insights for retail category managers because they are able to compute the impact on the marginal shares caused by a change in the average price of one brand or product.eng
dc.language.isoeng-
dc.publisherElsevier-
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147442/PT-
dc.rightsembargoedAccesspor
dc.subjectCluster analysiseng
dc.subjectMarket segmentationeng
dc.subjectPanel dataeng
dc.subjectSequential quadratic programingeng
dc.titleExtracting clusters from aggregate panel data: a market segmentation studyeng
dc.typearticle-
dc.pagination277 - 288-
dc.publicationstatusPublicadopor
dc.peerreviewedyes-
dc.journalApplied Mathematics and Computation-
dc.distributionInternacionalpor
dc.volume296-
degois.publication.firstPage277-
degois.publication.lastPage288-
degois.publication.titleExtracting clusters from aggregate panel data: a market segmentation studyeng
dc.date.updated2019-03-22T11:03:56Z-
dc.description.versioninfo:eu-repo/semantics/publishedVersion-
dc.identifier.doi10.1016/j.amc.2016.10.012-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Matemáticaspor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-30583-
iscte.alternateIdentifiers.wosWOS:000389391100023-
iscte.alternateIdentifiers.scopus2-s2.0-84994504066-
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

Files in This Item:
acessibilidade
File Description SizeFormat 
1-s2.0-S0096300316306105-main.pdfVersão Editora540.69 kBAdobe PDFView/Open    Request a copy


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.