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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| Format: | conferenceObject |
| Language: | eng |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/10451/4713 |
| Country: | Portugal |
| Oai: | oai:repositorio.ul.pt:10451/4713 |
| Summary: | 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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