Parametric Empirical Bayes Rules for Selecting the Most Probable Multinomial Event.

Abstract

This document considers a multinominal population with k (> or = 2) cells and an associated probability vector. A cell associated with p(k) is called the most probable event. We are interested in selecting the most probable event. Let i denote the index of the selected cell. Under a loss function this statistical selection problem is studied via a parametric empirical Bayes approach. Two empirical Bayes selection rules are proposed. They are shown to be asymptotically optimal at least of order 0(exp(-c sub 1n)) for some positive constants c sub i = 1,2, where n is the number of accumulated past experience (observations) at hand.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1986
Accession Number
ADA176045

Entities

People

  • Shanti Gupta
  • Tachen Liang

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Computations
  • Data Science
  • Decision Theory
  • Inequalities
  • Information Science
  • Military Research
  • Multivariate Analysis
  • New York
  • Observation
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistical Decision Theory
  • Statistics
  • Theorems
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference