Estimation in Parametric Mixture Families.

Abstract

For parametric mixture distributions indexed by say a univariate parameter Theta we investigate estimation of g theta under squared error loss. First we propose a method for uniformly improving upon an unbaised estimator of g theta. Second we characterize Bayes estimators of g theta and give a simple complete class theorem. Finally we study the performance of empirical Bayes rules generated using the EM algorithm. Application in the context of the noncentral chi-square distribution provides examples.

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Document Details

Document Type
Technical Report
Publication Date
Aug 18, 1987
Accession Number
ADA183818

Entities

People

  • Alan E. Gelfand

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Computations
  • Convergence
  • Differential Equations
  • Equations
  • Estimators
  • Iterations
  • Linear Differential Equations
  • Mathematics
  • Maximum Likelihood Estimation
  • Military Research
  • Security
  • Statistical Algorithms
  • Statistics
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Analytical Mechanics
  • Regression Analysis.