TITLE:
The Performance of Robust Methods in Logistic Regression Model
AUTHORS:
Idriss Abdelmajid Idriss Ahmed, Weihu Cheng
KEYWORDS:
Logistic Regression, Maximum Likelihood Estimator, Robust Estimation, Outlier, Weighted Maximum Likelihood
JOURNAL NAME:
Open Journal of Statistics,
Vol.10 No.1,
February
28,
2020
ABSTRACT: Logistic regression is the most important tool for data analysis in various fields. The classical approach for estimating parameters is the maximum likelihood estimation, a disadvantage of this method is high sensitivity to outlying observations. Robust estimators for logistic regression are alternative techniques due to their robustness. This paper presents a new class of robust techniques for logistic regression. They are weighted maximum likelihood estimators which are considered as Mallows-type estimator. Moreover, we compare the performance of these techniques with classical maximum likelihood and some existing robust estimators. The results are illustrated depending on a simulation study and real datasets.The new estimators showed the best performance relative to other estimators.