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García Escudero, Luis Ángel Mayo Iscar, Agustín Riani, Marco 2021-12-20T10:58:44Z 2021-12-20T10:58:44Z 2021 Statistics and Computing, 2021, vol. 32, n. 1. 0960-3174 https://uvadoc.uva.es/handle/10324/50994 10.1007/s11222-021-10061-3 1 Statistics and Computing 32 1573-1375 Producción Científica A new methodology for constrained parsimonious model-based clustering is introduced, where some tuning parameter allows to control the strength of these constraints. The methodology includes the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases. This is done in a natural way by filling the gap among models and providing a smooth transition among them. The methodology provides mathematically well-defined problems and is also useful to prevent us from obtaining spurious solutions. Novel information criteria are proposed to help the user in choosing parameters. The interest of the proposed methodology is illustrated through simulation studies and a real-data application on COVID data. Ministerio de Economía y Competitividad (grant MTM2017-86061-C2-1-P) Junta de Castilla y León - FEDER (grants VA005P17 and VA002G18) CRoNoS COST y el proyecto “Estadísticas para la detección de fraudes, con aplicaciones para datos comerciales y estados financieros ”de la Universidad de Parma (grant IC1408) Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE application/pdf eng Springer info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2021 The Authors Atribución 4.0 Internacional Model-based clustering Mixture modeling Constraints 12 Matemáticas Constrained parsimonious model-based clustering info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://link.springer.com/article/10.1007/s11222-021-10061-3 SI