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Regression Models for Bivariate Loss Data

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  • David Scollnik

Abstract

This case study illustrates the analysis of two possible regression models for bivariate claims data. Estimates or forecasts of loss distributions under these two models are developed using two methods of analysis: (1) maximum likelihood estimation and (2) the Bayesian method. These methods are applied to two data sets consisting of 24 and 1,500 paired observations, respectively. The Bayesian analyses are implemented using Markov chain Monte Carlo via WinBUGS, as discussed in Scollnik (2001). A comparison of the analyses reveals that forecasted total losses can be dramatically underestimated by the maximum likelihood estimation method because it ignores the inherent parameter uncertainty.

Suggested Citation

  • David Scollnik, 2002. "Regression Models for Bivariate Loss Data," North American Actuarial Journal, Taylor & Francis Journals, vol. 6(4), pages 67-80.
    • Handle: RePEc:taf:uaajxx:v:6:y:2002:i:4:p:67-80
    DOI: 10.1080/10920277.2002.10596065
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    Cited by:

    1. Koissi, Marie-Claire & Shapiro, Arnold F., 2006. "Fuzzy formulation of the Lee-Carter model for mortality forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 39(3), pages 287-309, December.

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