Ranking, estimation and hypothesis testing in unbalanced two-way additive models - a bayesian approach

Duncan K.H. Fong, James O. Berger

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

For the usual 2-factor additive model, there has been comparatively little work in the ranking and selection literature for the case of unequal sample sizes (unequal variances). The existing papers (e.g., Huang and Panchapakesan (1976), Dudewicz (1977), Gupta and Hsu (1980), Taneja and Dudewicz (1982), and Bechhofer and Dunnett (1987)) do not give explicit procedures unless assuming an equal number of observations and equal variances. (In the 2 x 2 case, classical ranking and selection procedures do exist even for more complicated models. See Taneja and Dudewicz (1984, 1987).) However the case of unequal sample sizes may arise in many natural settings, say in the problem of designing an experiment for comparing treatments in the presence of blocks of different fixed sizes, where one may assign an equal number of experimental units to each treatment within the same block. Unequal sample sizes can be handled in the classical analysis of variance (AOV) model (see Bishop and Dudewicz (1978, 1981) and Dudewicz and Bishop (1981)), which may partly explain the popularity of AOV. A Bayesian approach to the problem is taken here, leading to computation of the posterior probabilities that each treatment mean is the largest. In addition, a Bayesian version of AOV will be considered. Calculation of the quantities of interest involves, at worst, 5-dimensional numerical integration, for which an efficient Monte Carlo method of evaluation is given. An example is presented to illustrate the methodology.

Original languageEnglish (US)
Pages (from-to)1-24
Number of pages24
JournalStatistics and Risk Modeling
Volume11
Issue number1
DOIs
StatePublished - Jan 1993

Fingerprint

Additive Models
Hypothesis Testing
Analysis of variance (ANOVA)
Unequal
Bayesian Approach
Ranking
Analysis of variance
Ranking and Selection
Sample Size
Testing
Factor Models
Posterior Probability
Selection Procedures
Monte Carlo methods
Monte Carlo method
Numerical integration
Assign
Unit
Additive models
Hypothesis testing

All Science Journal Classification (ASJC) codes

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

Cite this

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Ranking, estimation and hypothesis testing in unbalanced two-way additive models - a bayesian approach. / Fong, Duncan K.H.; Berger, James O.

In: Statistics and Risk Modeling, Vol. 11, No. 1, 01.1993, p. 1-24.

Research output: Contribution to journalArticle

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