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Robustness against separation and outliers in logistic regression

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  • Rousseeuw, Peter J.
  • Christmann, Andreas

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  • Rousseeuw, Peter J. & Christmann, Andreas, 2003. "Robustness against separation and outliers in logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 43(3), pages 315-332, July.
    • Handle: RePEc:eee:csdana:v:43:y:2003:i:3:p:315-332
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    References listed on IDEAS

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    1. Huang, Yangxin, 2001. "Interval estimation of the ED50 when a logistic dose-response curve is incorrectly assumed," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 525-537, June.
    2. Andreas Christmann & Paul Fischer & Thorsten Joachims, 2002. "Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications," Computational Statistics, Springer, vol. 17(2), pages 273-287, July.
    3. Intrator, Orna & Intrator, Nathan, 2001. "Interpreting neural-network results: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 373-393, September.
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    Cited by:

    1. Christmann, Andreas, 2004. "Regression depth and support vector machine," Technical Reports 2004,54, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
    3. Gerhard Tutz & Jan Gertheiss, 2014. "Rating Scales as Predictors—The Old Question of Scale Level and Some Answers," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 357-376, July.
    4. Kweh, Qian Long & Tebourbi, Imen & Lo, Huai-Chun & Huang, Cheng-Tsu, 2022. "CEO compensation and firm performance: Evidence from financially constrained firms," Research in International Business and Finance, Elsevier, vol. 61(C).
    5. Tutz, Gerhard & Leitenstorfer, Florian, 2006. "Response shrinkage estimators in binary regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2878-2901, June.
    6. Knox, Kris Joseph & Blankmeyer, Eric C. & Trinidad, José A. & Stutzman, J.R., 2009. "Predicting bankruptcy in the Texas nursing facility industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 1047-1064, August.
    7. Hana Šinkovec & Angelika Geroldinger & Georg Heinze, 2019. "Bring More Data!—A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size," IJERPH, MDPI, vol. 16(23), pages 1-12, November.
    8. Elena Castilla & Abhik Ghosh & Nirian Martin & Leandro Pardo, 2021. "Robust semiparametric inference for polytomous logistic regression with complex survey design," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 701-734, September.
    9. Croux, Christophe & Haesbroeck, Gentiane, 2003. "Implementing the Bianco and Yohai estimator for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 273-295, October.
    10. Christmann, Andreas, 2004. "On a strategy to develop robust and simple tariffs from motor vehicle insurance data," Technical Reports 2004,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Ying Guan & Guang-Hui Fu, 2022. "A Double-Penalized Estimator to Combat Separation and Multicollinearity in Logistic Regression," Mathematics, MDPI, vol. 10(20), pages 1-19, October.
    12. Elok Heniwati & Angga Hervianto, 2024. "Logistic Regression to Predict BPK's Audit Opinion," Oblik i finansi, Institute of Accounting and Finance, issue 2, pages 118-127, June.
    13. Jaros³aw Kaczmarek, 2012. "Construction Elements Of Bankruptcy Prediction Models In Multi–Dimensional Early Warning Systems," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 5(1), pages 136-149, June.

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