Asymptotics for Configural Estimators.

Abstract

This paper examines the asymptotic properties of compromise estimators. By this we mean an estimation method which compromises between a finite number of sampling situations in a small sample optimal way. We develop the asymptotic theory of such estimators and show that under a specific choice of sampling situations the compromise estimator is asymptotically robust in Huber's sense. Originator-supplied keywords include: Robust estimation, Conditional inference, Equivariance, Asymptotics.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1984
Accession Number
ADA150106

Entities

People

  • S. Morgenthaler

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Arithmetic
  • Asymptotic Series
  • California
  • Contamination
  • Decision Theory
  • Estimators
  • Language
  • Mathematical Analysis
  • Military Research
  • New York
  • Optimal Estimators
  • Sampling
  • Statistical Analysis
  • Statistics
  • Symmetry
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms