KaydedilenlerDerginizi ekleyin

Dergiler / 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
Sayfa
1572–1582
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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 Maddede Bu Makale

🎯
Amaç & Hipotez:In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model.
📊
Temel Bulgular & Veri:Monte Carlo studies are evaluated the finite-sample performance of the proposed method.
💡
Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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.

🔖

Bu Makaleye Atıf Yap

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

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (17)

  1. A. Bergesio and V.J. Yohai, Projection estimators for generalized linear models, J. Amer. Statist. Assoc. 106 (494), 661-671, 2011
  2. A.M. Bianco and V.J. Yohai, Robust Estimation in the Logistic Regression Model, Robust Statistics, Data analysis, and Computer Intensive methods, Springer, 1996
  3. Z. Bursac, C.H. Gaussh, D.K. Williams and D.W. Hosmer, Purposeful selection of variables in logistic regression, Source Code Biol. Med. 3 (1), 1-8, 2008
  4. M.H. Chen, J.G. Ibrahim and C. Yiannoutsos, Prior elicitation, variable selection and Bayesian computation for logistic regression models, J. R. Stat. Soc. Ser. B. Stat. Methodol. 61 (1), 223-242, 1999
  5. P. Čížek, Trimmed likelihood-based estimation in binary regression models, Austrian J. Stat. 35 (2&3), 223-232, 2006
  6. P. Číźek, Robust and efficient adaptive estimation of binary-choice regression models, J. Amer. Statist. Assoc. 103 (482), 687-696, 2008
  7. C. Croux, C. Flandre and G. Haesbroeck, The breakdown behavior of the maximum likelihood estimator in the logistic regression model, Statist. Probab. Lett. 60 (4), 377-386, 2002
  8. L. Davies, The asymptotics of Rousseeuw’s minimum volume ellipsoid estimator, Ann. Statist. 20 (4), 1828-1843, 1992
  9. D. Gervini, Robust adaptive estimators for binary regression models, J. Statist. Plann. Inference 131 (2), 297-311, 2005
  10. D. Gervini and V.J. Yohai, A class of robust and fully efficient regression estimators, Ann. Statist. 30 (2), 583-616, 2002
  11. M. Guns and V. Vanacker, Logistic regression applied to natural hazards: Rare event logistic regression with replications, Nat. Hazard Earth Sys. 12 (6), 1937-1947, 2012
  12. Y. Güney, Y. Tuac, S. Özdemir and O. Arslan, Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution, Comput. Statist. 36 (2), 805-827, 2021
  13. D.R. Hunter and K. Lange, Quantile regression via an MM algorithm, J. Comput. Graph. Statist. 9 (1), 60-77, 2000
  14. D.R. Hunter and K. Lange, A tutorial on MM algorithms, Amer. Statist. 58 (1), 30-37, 2004
  15. Y. Jiang, Y.G. Wang, L.Y. Fu and X. Wang, Robust estimation using modified Huber’s functions with new tails, Technometrics 61 (1), 111-122, 2019
  16. R.J. Karunamuni, L.L. Kong and W. Tu, Efficient robust doubly adaptive regularized regression with applications, Stat. Methods Med. Res. 28 (7), 2210-2226, 2019
  17. R.L. Kennedy, A.M. Burton, H.S. Fraser, L.N. McStay and R.F. Harrison, Early diagnosis of ac