Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62974
Title: A Bayesian approach to the estimation of Pareto distribution with an application in insurance
Authors: Pang, WK
Hou, SH
Troutt, MD
Yu, BWT
Keywords: Pareto distribution
Markov chain Monte Carlo
Maximum likelihood estimation
Bayesian estimation
Issue Date: 2007
Publisher: Pushpa Publishing House
Source: Advances and applications in statistics, 2007, v. 7, no. 3, p. 389-401 How to cite?
Journal: Advances and applications in statistics 
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.
URI: http://hdl.handle.net/10397/62974
ISSN: 0972-3617
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