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Journals / Hacettepe Journal of Mathematics and Statistics / 2021 / Cilt: 50 - Sayı: 5

Robust variable selection in the logistic regression model

Yingqiang Huang
Yunlu Jiang
Jiantao Zhang
Hang Zou
Meilan Huang
Fanhong Chen
Pages
1572–1582
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model.
📊
Key Findings & Data:Monte Carlo studies are evaluated the finite-sample performance of the proposed method.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Logistic regression · Variable selection · Robustness

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.

🔖

Cite This Article

Huang, Y., Jiang, Y., Zhang, J., Zou, H., Huang, M., Chen, F. (2022). Robust variable selection in the logistic regression model. Hacettepe Journal of Mathematics and Statistics, Cilt: 50 - Sayı: 5, 1572–1582. https://ajindex.com/articles/529538

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