IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb475/200416.html
   My bibliography  Save this paper

On a strategy to develop robust and simple tariffs from motor vehicle insurance data

Author

Listed:
  • Christmann, Andreas

Abstract

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:sfb475:200416
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/49326/1/384012159.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Christmann, Andreas & Rousseeuw, Peter J., 1999. "Measuring overlap in logistic regression," Technical Reports 1999,25, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Christmann, Andreas & Steinwart, Ingo, 2003. "On robustness properties of convex risk minimization methods for pattern recognition," Technical Reports 2003,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christmann, Andreas, 2004. "Regression depth and support vector machine," Technical Reports 2004,54, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," 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. 11(3), pages 445-466, September.
    3. Christmann, Andreas & Steinwart, Ingo, 2003. "On robustness properties of convex risk minimization methods for pattern recognition," Technical Reports 2003,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    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. Fijorek, Kamil & Sokolowski, Andrzej, 2012. "Separation-Resistant and Bias-Reduced Logistic Regression: STATISTICA Macro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(c02).
    6. 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.
    7. 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.
    8. Tatjana Lange & Karl Mosler & Pavlo Mozharovskyi, 2014. "Fast nonparametric classification based on data depth," Statistical Papers, Springer, vol. 55(1), pages 49-69, February.
    9. 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.
    10. 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.
    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. 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.
    13. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
    14. 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.
    15. Tutz, Gerhard & Leitenstorfer, Florian, 2006. "Response shrinkage estimators in binary regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2878-2901, June.
    16. Dyckerhoff, Rainer & Mozharovskyi, Pavlo & Nagy, Stanislav, 2021. "Approximate computation of projection depths," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    17. Pavlo Mozharovskyi & Karl Mosler & Tatjana Lange, 2015. "Classifying real-world data with the $${ DD}\alpha $$ D D α -procedure," 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. 9(3), pages 287-314, September.
    18. Nedret Billor & Asheber Abebe & Asuman Turkmen & Sai Nudurupati, 2008. "Classification Based on Depth Transvariations," Journal of Classification, Springer;The Classification Society, vol. 25(2), pages 249-260, November.
    19. 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.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:sfb475:200416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/isdorde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.