A bayesian approach to the estimation of the largest normal mean

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A hierarchical Bayesian approach to the problem of estimating the largest normal mean is considered. Calculation of the posterior mean and the posterior variance involves, at worst, 3-dimensional numerical integration, for which an efficient Monte Carlo method of evaluation is given. An example is presented to illustrate the methodology. In the two populations case, computation of the posterior estimates can be substantially simplified and in special cases can actually be performed using closed form solutions. A simulation study has been done to compare mean square errors of some hierarchical Bayesian estimators that are expressed in closed forms and several existing estimators of the larger mean.

Original languageEnglish (US)
Pages (from-to)119-133
Number of pages15
JournalJournal of Statistical Computation and Simulation
Volume40
Issue number1-2
DOIs
StatePublished - Feb 1 1992

Fingerprint

Bayesian Approach
Posterior Mean
Bayesian Estimator
Closed-form Solution
Mean square error
Monte Carlo method
Numerical integration
Closed-form
Monte Carlo methods
Simulation Study
Estimator
Methodology
Evaluation
Estimate
Bayesian approach
Simulation study
Closed-form solution

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

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A bayesian approach to the estimation of the largest normal mean. / Fong, Duncan K.H.

In: Journal of Statistical Computation and Simulation, Vol. 40, No. 1-2, 01.02.1992, p. 119-133.

Research output: Contribution to journalArticle

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