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Using R In Generalized Linear Models

Author

Listed:
  • Mihaela Covrig

    (Bucharest University of Economic Studies)

  • Iulian Mircea

    (Bucharest University of Economic Studies)

  • Gheorghita Zbaganu

    (University of Bucharest)

  • Alexandru Coser

    (Vodafone Romania)

  • Alexandru Tindeche

    (BCR Life Insurance)

Abstract

This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the glm routine package in R. Particularly, regression models will be analyzed for those cases in which the explained variable follows a Poisson or a Negative Binomial distribution. The paper will briefly present the GLM methodology for count data, while the practical part will revolve around estimating and comparing models in which the response variable shows the number of claims in a portfolio of automobile insurance policies.

Suggested Citation

  • Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    • Handle: RePEc:rsr:journl:v:63:y:2015:i:3:p:33-45
    as

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    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Boucher, Jean-Philippe & Inoussa, Rofick, 2014. "A Posteriori Ratemaking With Panel Data," ASTIN Bulletin, Cambridge University Press, vol. 44(3), pages 587-612, September.
    3. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149.
    4. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    5. Yip, Karen C.H. & Yau, Kelvin K.W., 2005. "On modeling claim frequency data in general insurance with extra zeros," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 153-163, April.
    6. Frees, Edward W. & Valdez, Emiliano A., 2008. "Hierarchical Insurance Claims Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1457-1469.
    7. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2014. "Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 225-249.
    8. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.
    9. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2014. "Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape," LIDAM Reprints ISBA 2014006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Jean-Philippe Boucher & Michel Denuit & Montserrat Guillén, 2007. "Risk Classification for Claim Counts," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 110-131.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    GLM; count data; insurance; Poisson regression; Negative Binomial Regression; R;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    Statistics

    Access and download statistics

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