A BAYESIAN APPROACH TO THE ESTIMATION OF PARETO DISTRIBUTION WITH AN APPLICATION IN INSURANCE

被引：0

作者：

Pang, Wan Kai ^{[1
]}

Hou, Shui Hung ^{[1
]}

Troutt, Marvin D. ^{[2
]}

Yu, Bosco Wing Tong ^{[3
]}

机构：

[1] Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China

[2] Kent State Univ, Coll Business Adm, Dept Management & Informat Syst, Kent, OH 44242 USA

[3] Hong Kong Polytech Univ, Sch Accounting & Finance, Hong Kong, Hong Kong, Peoples R China

来源：

ADVANCES AND APPLICATIONS IN STATISTICS | 2007年 / 7卷 / 03期

关键词：

Pareto distribution; Markov Chain Monte Carlo; maximum likelihood estimation; Bayesian estimation;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：389 / 401

页数：13

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