TITLE:
Robust Estimators for Poisson Regression
AUTHORS:
Idriss Abdelmajid Idriss, Weihu Cheng
KEYWORDS:
Poisson Regression Model, Maximum Likelihood Estimator, Robust Estimation, Contaminated Model, Weighted Maximum Likelihood Estimator
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.1,
February
28,
2023
ABSTRACT: The present paper proposes a new
robust estimator for Poisson regression models. We used the weighted maximum
likelihood estimators which are regarded as Mallows-type estimators. We
perform a Monte Carlo simulation study to assess the performance 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 contains outliers.
The proposed estimators showed the best performance compared to other robust
estimators.