Bayesian sequential methods for choosing the better of two binomial population

Jones, Peter W.; Madhi, Saad Abed

doi:10.1080/07474948608836096

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The problem has been discussed in Bechhofer and Kulkarni (1982), Bechhofer and Frisardi (1982) and Jennison (1984), and a formal optimal decision theoretic formulation has been given in Jones and Madhi (1985).…”

Section: Introductionmentioning

confidence: 99%

A simulation study of some suboptimal bayesian sequential sampling schemes for choosing the better of two binomial populations

Jones

Madhi

1986

Journal of Statistical Computation and Simulation

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Bayesian sequential schemes which use the posterior estimates of the unknown probabilities for choosing the better of two binomial populations are studied. Single sequential, group sequential and fixed sample size schemes are compared under criteria such as the probability of correct selection, expected sample size and expected number of observations on the poorer population which are obtained by Monte Carlo simulation. A modification of a rule due to Bechhofer and Kulkarni (1981) and the play the clear winner rule are also considered.

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Section: Introductionmentioning

confidence: 99%

A simulation study of some suboptimal bayesian sequential sampling schemes for choosing the better of two binomial populations

Jones

Madhi

1986

Journal of Statistical Computation and Simulation

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Bayesian inference for categorical data analysis

Agresti

Hitchcock

2005

JISS

133

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This article surveys Bayesian methods for categorical data analysis, with primary emphasis on contingency table analysis. Early innovations were proposed by Good (1953Good ( , 1956Good ( , 1965 for smoothing proportions in contingency tables and by Lindley (1964) for inference about odds ratios. These approaches primarily used conjugate beta and Dirichlet priors. Altham (1969Altham ( , 1971 presented Bayesian analogs of small-sample frequentist tests for 2×2 tables using such priors. An alternative approach using normal priors for logits received considerable attention in the 1970s by Leonard and others (e.g., Leonard 1972). Adopted usually in a hierarchical form, the logit-normal approach allows greater flexibility and scope for generalization. The 1970s also saw considerable interest in loglinear modeling. The advent of modern computational methods since the mid-1980s has led to a growing literature on fully Bayesian analyses with models for categorical data, with main emphasis on generalized linear models such as logistic regression for binary and multi-category response variables.

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Bayesian minimum sample size designs for the bernoulli selection problem

Jones

Madhi

1988

Sequential Analysis

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Bayesian sequential methods for choosing the better of two binomial population

Cited by 3 publications

References 7 publications

A simulation study of some suboptimal bayesian sequential sampling schemes for choosing the better of two binomial populations

A simulation study of some suboptimal bayesian sequential sampling schemes for choosing the better of two binomial populations

Bayesian inference for categorical data analysis

Bayesian minimum sample size designs for the bernoulli selection problem

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