A Comparison of Estimation Techniques for the Three Parameter Pareto Distribution

Abstract

The purpose of this thesis is to compare the minimum distance estimation technique with the best linear unbiased estimation technique to determine which estimator provides more accurate estimates of the underlying location and scale parameter values for a given Pareto distribution. Two forms of the Kolmogorov, Anderson-Darling, and Cramer-von Mises minimum distance estimators are tested. A Monte Carlo methodology is used to generate the Pareto random variates and the resulting estimates. A mean square error comparison is then performed to evaluate which estimator provides the best results. Additionally, various sample sizes and shape parameters are also used to determine whether they have an influence on a given estimator's performance.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1985
Accession Number
ADA163831

Entities

People

  • Dennis J. Charek

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Computer Programming
  • Computer Programs
  • Computers
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Monte Carlo Method
  • Order Statistics
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Distributions
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.