Convergence Results for Sequential Estimation of the Largest Mean.

Abstract

We consider the sequential estimation of the largest mean of k populations when the observations are normally distributed with a common unknown variance and the goal is to control the mean square error (MSE) at a prespecified level. By eliminating from the experiment populations which the data indicate are not associated with the largest mean, it is shown that, compared to existing procedures, significant savings in sample size can be obtained. Weak convergence results are obtained for the stopping times and the estimate of the largest mean as consequences of more general results; these are used to compute the asymptotic MSE.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA072640

Entities

People

  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Brownian Motion
  • Convergence
  • Elimination
  • Equations
  • New York
  • North Carolina
  • Observation
  • Probability
  • Scientific Research
  • Standards
  • Statistics
  • Stochastic Processes
  • Universities
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Mathematical Modeling and Probability Theory.