A Bayesian approach to parameter estimation for kernel density estimation via transformations

Qing Liu, David Pitt, Xibin Zhang & Xueyuan Wu
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 exhibit 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...
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