Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/26551
Título: | Comparison of mixture and classification maximum likelihood approaches in poisson regression models |
Autor(es): | Faria, Susana Soromenho, Gilda |
Palavras-chave: | Maximum likelihood estimation EM algorithm Classification EM algorithm Mixture poisson regression models Simulation study |
Data: | Ago-2008 |
Editora: | Springer |
Resumo(s): | In this work, we propose to compare two algorithms to compute maximum likelihood estimators of the parameters of a mixture Poisson regression models. To estimate these parameters, we may use the EM algorithm in a mixture approach or the CEM algorithm in a classification approach. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets in a target number of iterations. Simulation results show that the CEM algorithm is a good alternative to the EM algorithm for fitting Poisson mixture regression models, having the advantage of converging more quickly. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/26551 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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artigo15-01.pdf Acesso restrito! | Documento Principal | 207 kB | Adobe PDF | Ver/Abrir |