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ON EMPIRICAL BAYES STOPPING TIMES
Author(s) -
Karunamuni Rohan
Publication year - 1989
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1989.tb00401.x
Subject(s) - bayes' theorem , optimal stopping , bayes' rule , decision rule , bayes error rate , function (biology) , stopping time , mathematics , optimal decision , sample (material) , decision theory , decision problem , computer science , bayes factor , mathematical optimization , statistics , bayes classifier , bayesian probability , artificial intelligence , algorithm , decision tree , chemistry , chromatography , evolutionary biology , biology
Summary We consider the empirical Bayes decision theory where the component problems are the optimal fixed sample size decision problem and a sequential decision problem. With these components, an empirical Bayes decision procedure selects both a stopping rule function and a terminal decision rule function. Empirical Bayes stopping rules are constructed for each case and the asymptotic behaviours are investigated.