Abstract
Minimum Hellinger distance estimation (MHDE) has been shown to discount anomalous data points in a smooth manner with first-order efficiency for a correctly specified model. An estimation approach is proposed for finite mixtures of Poisson regression models based on MHDE. Evidence from Monte Carlo simulations suggests that MHDE is a viable alternative to the maximum likelihood estimator when the mixture components are not well separated or the model parameters are near zero. Biometrical applications also illustrate the practical usefulness of the MHDE method.
| Original language | English |
|---|---|
| Pages (from-to) | 1016-1026 |
| Journal | Biometrics |
| Volume | 59 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Dec 2003 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Finite mixtures of Poisson regression models
- Maximum likelihood estimation
- Minimum Hellinger distance
- Outliers
- Robustness
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