An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions
We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.
|Date of creation:||Mar 2010|
|Date of revision:||Mar 2010|
|Contact details of provider:|| Postal: Fundació Bosch i Gimpera, C. Baldiri i Reixac, 4-8, 08028 Barcelona|
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"Kernel Density Estimation of Actuarial Loss Functions,"
Finance Working Papers
00-4, University of Aarhus, Aarhus School of Business, Department of Business Studies.
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