Robust variable selection in the logistic regression model

Authors : Yunlu JIANG, Jianto ZHANG, Yingqiang HUANG, Hang ZOU, Meilan HUANG, Fanhong CHEN

Pages : 1572-1582

Doi:10.15672/hujms.810383

View : 25 | Download : 12

Publication Date : 2021-10-15

Article Type : Research Paper

Abstract :In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model. The proposed method is robust to outliers and considers the goodness-of-fit of the regression model. Furthermore, we apply an MM algorithm to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed method. The results show that when there are outliers in the dataset or the distribution of covariate variable deviates from the normal distribution, the finite-sample performance of the proposed method is better than that of other existing methods. Finally, the proposed methodology is applied to the data analysis of Parkinson`s disease.
Keywords : Logistic regression, Variable selection, Robustness, MM algorithm