Open AccessEfficient estimation of Pareto model: Some modified percentile estimators
Author(s) -
Sajjad Haider Bhatti,
Saghir Hussain,
Tanvir Ahmad,
Muhammad Aslam,
Muhammad Aftab,
Muhammad Ali Raza
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0196456
Subject(s) - estimator , percentile , statistics , mean squared error , mathematics , statistic , cumulative distribution function , pareto distribution , monte carlo method , m estimator , order statistic , probability density function
The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.