The Performance of Robust Methods in Logistic Regression Model

• 作者: Idriss Abdelmajid Idriss Ahmed Weihu Cheng
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: LOGISTIC Regression MAXIMUM LIKELIHOOD ESTIMATOR Robust Estimation OUTLIER WEIGHTED MAXIMUM LIKELIHOOD
• 年，卷（期）: 2020,（1）
• 页码范围: 127-138
• 页数: 12页
• 分类号: O17

摘要

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.