Yingqiang Huang, Yunlu Jiang, Jiantao Zhang, Hang Zou, Meilan Huang, Fanhong Chen

Robust variable selection in the logistic regression model

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,

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Hacettepe Journal of Mathematics and Statistics-Cover

Yayın Aralığı: Yılda 6 Sayı
Başlangıç: 2002
Yayıncı: Hacettepe Üniversitesi Fen Fakultesi

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