Comparison of mixture and classification maximum likelihood approaches in poisson regression models
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 t...
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| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2008
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| Texto completo: | http://hdl.handle.net/1822/26551 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/26551 |
| Resumo: | 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. |
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