Likelihood for random-effect models (invited article)
HolaFor inferences from random-effect models Lee and Nelder (1996) proposed to use hierarchical likelihood (h-likelihood). It allows inference from models that may include both fixed and random parameters. Because of the presence of unobserved random variables h-likelihood is not a likelihood in the...
| Autores: | , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2005 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3768 |
| Acesso em linha: | https://hdl.handle.net/2099/3768 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Inference Inferència Classificació AMS::62 Statistics::62F Parametric inference |
| Resumo: | HolaFor inferences from random-effect models Lee and Nelder (1996) proposed to use hierarchical likelihood (h-likelihood). It allows inference from models that may include both fixed and random parameters. Because of the presence of unobserved random variables h-likelihood is not a likelihood in the Fisherian sense. The Fisher likelihood framework has advantages such as generality of application, statistical and computational efficiency. We introduce an extended likelihood framework and discuss why it is a proper extension, maintaining the advantages of the original likelihood framework. The new framework allows likelihood inferences to be drawn for a much wider class of models. |
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