Inference for the Binomial N Parameter: A Bayes Empirical Bayes Approach. Revision.

Abstract

The problem of inference about the binomial N parameter is considered. Applications arise in situations where an unknown population size is to be estimated. Previous work has focused on point estimation, but many applications require interval estimation, prediction, and decision-making. A Bayes empirical Bayes approach is presented. This provides a simple and flexible way of specifying prior information, and also allows a convenient representation of vague prior knowledge. It yields solutions to the problems interval estimation, prediction, and decision-making, as well as that of point estimation. The Bayes estimator compares favorably with the best, previously proposed, point estimators in the literature. The Bayesian estimation interval which corresponds to a vague prior distribution also performs satisfactorily when used as a frequentist confidence interval.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1987
Accession Number
ADA183432

Entities

People

  • Adrian Raftery

Organizations

  • University of Washington

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Animals
  • Bayesian Networks
  • Binomials
  • Computer Science
  • Data Sets
  • Estimators
  • Intervals
  • Literature
  • Method Of Moments
  • Models
  • National Parks
  • Probability
  • Random Variables
  • Simulations
  • Statistical Analysis
  • Statistics
  • Systems Science

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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