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Autordc.contributor.authorNilo-Poyanco, Ricardo [Univ Mayor, Fac Ciencias, Escuela Biotecnol, Santiago, Chile]es_CL
Autordc.contributor.authorYoung, Derek S.es_CL
Autordc.contributor.authorChen, Xies_CL
Autordc.contributor.authorHewage, Dilrukshi C.es_CL
Fecha registrodc.date.accessioned2020-04-12T14:11:55Z
Fecha registrodc.date.accessioned2020-04-14T15:37:51Z
Fecha disponibledc.date.available2020-04-12T14:11:55Z
Fecha disponibledc.date.available2020-04-14T15:37:51Z
Año de Publicacióndc.date.issued2019es_CL
dc.identifier.citationYoung, D. S., Chen, X., Hewage, D. C., & Nilo-Poyanco, R. (2019). Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering. Advances in Data Analysis and Classification, 13(4), 1053-1082.es_CL
dc.identifier.issn1862-5347es_CL
dc.identifier.issn1862-5355es_CL
URL directadc.identifier.urihttps://doi.org/10.1007/s11634-019-00361-yes_CL
URL directadc.identifier.urihttp://repositorio.umayor.cl/xmlui/handle/sibum/6528
Resumendc.description.abstractFinite mixtures of (multivariate) Gaussian distributions have broad utility, including their usage for model-based clustering. There is increasing recognition of mixtures of asymmetric distributions as powerful alternatives to traditional mixtures of Gaussian and mixtures of t distributions. The present work contributes to that assertion by addressing some facets of estimation and inference for mixtures-of-gamma distributions, including in the context of model-based clustering. Maximum likelihood estimation of mixtures of gammas is performed using an expectation-conditional-maximization (ECM) algorithm. The Wilson-Hilferty normal approximation is employed as part of an effective starting value strategy for the ECM algorithm, as well as provides insight into an effective model-based clustering strategy. Inference regarding the appropriateness of a common-shape mixture-of-gammas distribution is motivated by theory from research on infant habituation. We provide extensive simulation results that demonstrate the strong performance of our routines as well as analyze two real data examples: an infant habituation dataset and a whole genome duplication dataset.es_CL
Idiomadc.language.isoenes_CL
Editordc.publisherSPRINGER HEIDELBERGes_CL
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceAdv. Data Anal. Classif., DIC, 2019. 13(4): p. 1053-1082
Materiadc.subjectStatistics & Probabilityes_CL
Titulodc.titleFinite mixture-of-gamma distributions: estimation, inference, and model-based clusteringes_CL
Tipo Documentodc.typeArtículoes_CL
umayor.facultadCIENCIAS
umayor.politicas.sherpa/romeoRoMEO green journal (Se puede archivar el pre-print y el post-print o versión de editor/PDF). Disponible en: http://sherpa.ac.uk/romeo/index.phpes_CL
umayor.indexadoWOS:000496565800010es_CL
umayor.indexadoSIN PMIDes_CL
dc.identifier.doiDOI: 10.1007/s11634-019-00361-yes_CL]
umayor.indicadores.wos-(cuartil)Q1es_CL
umayor.indicadores.scopus-(scimago-sjr)SCIMAGO/ INDICE H: 23 Hes_CL


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