Improved Estimators in Simultaneous Estimation of Scale Parameters.

Abstract

A general class of estimators is developed for improving upon best scale invariant estimators of two or more arbitrary scale parameters (or powers thereof) for arbitrary positive distributions with sufficient moments under weighted squared error loss function. The technique is to compute the risk difference in terms of moments of the distribution. Some conditions are obtained under which the maximum improvement is possible, and the form of the estimator can be chosen to achieve this maximum along any specified ray. The result is then extended to the estimation of a linear transform of the parameter vector. Finally, some examples are given with numerical calculations to obtain the amount of risk improvement.

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Document Details

Document Type
Technical Report
Publication Date
Dec 10, 1987
Accession Number
ADA191108

Entities

People

  • Alan E. Gelfand
  • Dipak K. Dey

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computations
  • Estimators
  • Inequalities
  • Mathematics
  • Military Research
  • Normal Distribution
  • Notation
  • Statistics
  • Three Dimensional
  • Two Dimensional
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Image Processing and Computer Vision.
  • Statistical inference.

Technology Areas

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