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Robust Estimators for Poisson Regression
College of Applied Science, Department of Statistics, Beijing University of Technology, Beijing, China
College of Applied Science, Department of Statistics, Beijing University of Technology, Beijing, China
- 1 College of Applied Science, Department of Statistics, Beijing University of Technology, Beijing, China
- 2 College of Applied Science, Department of Statistics, Beijing University of Technology, Beijing, China
Open Journal of Statistics·Volume 13 (2023)·Pages 112–118·Published 17 February 2023·DOI10.4236/ojs.2023.131007
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Abstract
The present paper propose s a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimator s . We perform a Monte Carlo simulation study to assess the perform ance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general , all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contain s outliers. The proposed estimators showed the best performance compared to other robust estimators.
KeywordsPoisson Regression ModelMaximum Likelihood EstimatorRobust EstimationContaminated ModelWeighted Maximum Likelihood Estimator
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