Robust Estimation of Mixture Constants

Abstract

The problem of estimating the mixture constant for a mixture process is investigated. The asymptotic properties of M-Estimators are discussed. Conditions are given under which such estimators are asymptotically normal and consistent. It is shown that the maximum likelihood estimate and estimates based on moments satisfy these conditions. For instances in which there is uncertainty about the distributions composing the mixture distribution the robust estimator is found to be a censored version of the nominal MLE nonlinearity. Some numerical results on the existence of robust solutions are presented.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 27, 1987
Accession Number
ADA204890

Entities

People

  • Douglas J. Warren
  • John B. Thomas

Organizations

  • Princeton University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computer Science
  • Continents
  • Electrical Engineering
  • Engineering
  • Estimators
  • Gaussian Noise
  • Geographic Regions
  • Information Science
  • Mathematical Analysis
  • Military Research
  • New York
  • Noise
  • North America
  • Probability
  • Theses
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.