Binomial N Estimation. A Bayes Empirical Bayes Approach.

Abstract

A Bayes empirical Bayes approach to the problem of estimating N in the binomial distribution is presented. This provides a simple and flexible way of specifying prior information, and also allows a convenient representation of vague prior knowledge. In addition. it yields a solution to the interval estimation problem. The Bayes estimator corresponding to the relative squared error loss function and a vague prior distribution is shown to be stable, and to compare favorably with the estimators introduced by Olkin et al. (1981) and Carroll and Lombard (1985).

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1986
Accession Number
ADA181145

Entities

People

  • Adrian Raftery

Organizations

  • University of Washington

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Bayesian Networks
  • Binomials
  • Computations
  • Computer Science
  • Data Science
  • Data Sets
  • Estimators
  • Information Science
  • Intervals
  • Method Of Moments
  • Probability
  • Random Number Generators
  • Random Variables
  • Reliability
  • Simulations
  • Statistics
  • Systems Science

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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