Advances and Applications in Statistics
Volume 7, Issue 3, Pages 389 - 401
(December 2007)
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A BAYESIAN APPROACH TO THE ESTIMATION OF PARETO DISTRIBUTION WITH AN APPLICATION IN INSURANCE
Wan Kai Pang (Hong Kong), Shui Hung Hou (Hong Kong), Marvin D. Troutt (U. S. A.) and Bosco Wing Tong Yu (Hong Kong)
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Abstract: Pareto distribution plays an important role in modelling wealth and income distributions in economics. Parameter estimation of the two-parameter Pareto distribution has been studied by others in the past and a number of optimization schemes have been proposed. In this paper, we use the Markov Chain Monte Carlo (MCMC) technique to estimate the Pareto parameters. Some cases as well as a case of real data application from insurance are investigated. The study is quite successful and the method performed well in estimating the threshold parameter of the Pareto distribution. |
Keywords and phrases: Pareto distribution, Markov Chain Monte Carlo, maximum likelihood estimation, Bayesian estimation. |
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