Work

Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles

Year: 2015

Type: article

Abstract: 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.

Source: RECERCAT (Consorci de Serveis Universitaris de Catalunya)

Authors: Martí Casals, Klaus Langohr, Josep L. Carrasco, Lars Rönnegård

Institution: Universitat de Barcelona

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Generalitat de Catalunya, Ministerio de Economía y Competitividad

Awards: G3825852721, G7200152763, G930487962, G940347865, G985971609

OpenAlex ID: W2239698213

FWCI: 1.224

Cites: 62

Cited by: 12

Related to: 10