Journal article

A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations

Q Liu, D Pitt, X Zhang, X Wu

Annals of Actuarial Science | Cambridge University Press | Published : 2011


In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibits a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original data does not perform well. However, the density of the original data can be estimated through estimating the density of the transformed data using kernels. It is well known that the performance of a kernel density estimator is mainly determined by the bandwidth, and only in a minor way by the kernel. ..

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University of Melbourne Researchers