Log-ratio methods in mixture models for compositional data sets
When traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are re...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/112749 |
| Acceso en línea: | https://hdl.handle.net/2117/112749 |
| Access Level: | acceso abierto |
| Palabra clave: | Compositional data Finite Mixture Log ratio Model-based clustering Normal distribution Orthonormal coordinates Simplex Classificació AMS::62 Statistics::62E Distribution theory Classificació AMS::62 Statistics::62G Nonparametric inference Classificació AMS::62 Statistics::62H Multivariate analysis Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | When traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are revisited and the major difficulties are detailed. A new proposal using a mixture of distributions defined on orthonormal log-ratio coordinates is introduced. A real data set analysis is presented to illustrate and compare the different methodologies. |
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