An application of mixture distributions in modelization of length of hospital stay
Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness exhibited by this variable poses problems in statistical modeling. The aim of this work is to model the variable LOS within diagnosis-related groups (DRG) through finite mixtu...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/114724 |
| Acceso en línea: | https://hdl.handle.net/11441/114724 https://doi.org/10.1002/sim.3029 |
| Access Level: | acceso abierto |
| Palabra clave: | Length of stay Diagnosis-related groups Finite mixture model Gamma distributions Weibull distributions Lognormal distribution |
| Sumario: | Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness exhibited by this variable poses problems in statistical modeling. The aim of this work is to model the variable LOS within diagnosis-related groups (DRG) through finite mixtures of distributions. A mixture of the union of Gamma, Weibull and Lognormal families is used in the model, instead of a mixture of a unique distribution family. Some theoretical questions regarding the model, such as the identifiability and study of asymptotic properties of ML estimators, are analyzed. The EM algorithm is proposed for performing these estimators. Finally, this new proposed model is illustrated by using data from different DRGs. |
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