Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study
Generalized linear mixed models are flexible tools for modeling non-normal data and are usefulfor accommodating overdispersion in Poisson regression models with random effects. Theirmain difficulty resides in the parameter estimation because there is no analytic solution for themaximization of the m...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2015 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/88514 |
| Online Access: | https://hdl.handle.net/2117/88514 |
| Access Level: | Open access |
| Keyword: | Estimation methods overdispersion Poisson generalized linear mixed models simulation study statistical principles sport injuries Classificació AMS::62 Statistics::62F Parametric inference Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62P Applications Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Summary: | Generalized linear mixed models are flexible tools for modeling non-normal data and are usefulfor accommodating overdispersion in Poisson regression models with random effects. Theirmain difficulty resides in the parameter estimation because there is no analytic solution for themaximization of the marginal likelihood. Many methods have been proposed for this purpose andmany of them are implemented in software packages. The purpose of this study is to comparethe performance of three different statistical principles –marginal likelihood, extended likelihood,Bayesian analysis – via simulation studies. Real data on contact wrestling are used for illustration. |
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