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RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

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  • Jessica Pesantez-Narvaez

    (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain)

  • Montserrat Guillen

    (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain)

  • Manuela Alcañiz

    (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain)

Abstract

A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses.

Suggested Citation

  • Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2021. "RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach," Mathematics, MDPI, vol. 9(5), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:579-:d:513498
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    References listed on IDEAS

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    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    3. Yufei Jin & Roderick Rejesus & Bertis Little, 2005. "Binary choice models for rare events data: a crop insurance fraud application," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 841-848.
    4. Maalouf, Maher & Trafalis, Theodore B., 2011. "Robust weighted kernel logistic regression in imbalanced and rare events data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 168-183, January.
    5. Zaremba, Adam & Czapkiewicz, Anna, 2017. "Digesting anomalies in emerging European markets: A comparison of factor pricing models," Emerging Markets Review, Elsevier, vol. 31(C), pages 1-15.
    6. Carpenter, Daniel P. & Lewis, David E., 2004. "Political Learning from Rare Events: Poisson Inference, Fiscal Constraints, and the Lifetime of Bureaus," Political Analysis, Cambridge University Press, vol. 12(3), pages 201-232, July.
    7. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2021. "A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 281-309, January.
    8. Simon C. K. Lee & Sheldon Lin, 2018. "Delta Boosting Machine with Application to General Insurance," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(3), pages 405-425, July.
    9. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
    10. Cook, Scott J. & Hays, Jude C. & Franzese, Robert J., 2020. "Fixed effects in rare events data: a penalized maximum likelihood solution," Political Science Research and Methods, Cambridge University Press, vol. 8(1), pages 92-105, January.
    11. Bo, Lijun & Wang, Yongjin & Yang, Xuewei, 2010. "Markov-modulated jump-diffusions for currency option pricing," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 461-469, June.
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