Assessing the Behavior of Robust Estimates of Location in Small Samples: Introduction to Configural Polysampling.

Abstract

Configural polysampling offers an effective alternative to both usual Monte Carlo techniques and small sample asymptotics in studying and improving robust estimates. Attention here is restricted to location estimators that are location and scale invariant. The methods are applicable to both simple and compound sampling situations. Several possible aspects of such studies include: (a) the determination of the minimum attainable variance in each sampling situation, (b) the determination of the maximum attainable polyefficiency over several sampling situations, (c) the fine-tuning of robust estimates with the intent of increasing their polyefficiency for small n, and (d) the identification of data configurations where one can a priori expect poor performance with certain estimators. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1981
Accession Number
ADA105956

Entities

People

  • Daryl Pregibon
  • John W. Tukey

Organizations

  • Princeton University

Tags

DTIC Thesaurus Topics

  • Data Analysis
  • Data Science
  • Distribution Functions
  • Efficiency
  • Estimators
  • Gaussian Distributions
  • Identification
  • Information Science
  • Military Research
  • Monte Carlo Method
  • Optimal Estimators
  • Order Statistics
  • Random Variables
  • Sampling
  • Statistical Algorithms
  • Statistical Samples
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Acoustics.
  • Statistical inference.
  • Systems Analysis and Design