Minimum Distance and Robust Estimation.

Abstract

Robust and consistent of the location parameter of an asymmetric distribution and general, non-location and scale parameter estimation problems have been vexing problems in the history of robustness studies. The minimum distance (MD) estimation method is shown to provide a heuristically reasonable mode of attack for these problems which also leads to excellent robustness properties. Both asymptotic and Monte Carlo results for the familiar case of estimation of the location parameter of a symmetric distribution support this proposition, showing MD-estimators to be competitive with some of the better estimators thus far proposed. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 05, 1979
Accession Number
ADA085183

Entities

People

  • William C. Parr
  • William R. Schucany

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Normal Distribution
  • Normality
  • Probability
  • Random Variables
  • Sequences
  • Standards
  • Statistical Algorithms
  • Statistical Functions
  • Statistics
  • Stochastic Processes
  • Surveys
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Educational Psychology
  • Statistical inference.