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Zbl 1162.62080
Novikov, Andrey
Optimal sequential tests for two simple hypotheses.
(English)
[J] Sequential Anal. 28, No. 2, 188-217 (2009). ISSN 0747-4946; ISSN 1532-4176/e

Summary: A general problem of testing two simple hypotheses about the distribution of a discrete-time stochastic process is considered. The main goal is to minimize an average sample number over all sequential tests whose error probabilities do not exceed some prescribed levels. As a criterion of minimization, the average sample number under a third hypothesis is used [modified Kiefer-Weiss problem; {\it J. Kiefer} and {\it L. Weiss}, Ann. Math. Stat. 28, 57--74 (1957; Zbl 0079.35406)]. For a class of sequential testing problems, the structure of optimal sequential tests is characterized. An application to the Kiefer-Weiss problem for discrete-time stochastic processes is proposed. As another application, the structure of Bayes sequential tests for two composite hypotheses, with a fixed cost per observation, is given. The results are also applied for finding optimal sequential tests for discrete-time Markov processes. In a particular case of testing two simple hypotheses about a location parameter of an autoregressive process of order 1, it is shown that the sequential probability ratio test has the {\it A. Wald} and {\it J. Wolfowitz} optimality property [Ann. Math. Stat. 19, 326--339 (1948; Zbl 0032.17302)].
MSC 2000:
*62L10 Sequential statistical analysis
62L15 Optimal stopping (statistics)
62C10 Bayesian problems
62M07 Non-Markovian processes: hypothesis testing
62F15 Bayesian inference
62M02 Markov processes: hypothesis testing

Keywords: Bayesian sequential testing; composite hypotheses; dependent observations; discrete-time Markov process; discrete-time stochastic process; Kiefer-Weiss problem; optimal sequential test; sequential analysis; sequential hypothesis testing; sequential probability ratio test; two simple hypotheses

Citations: Zbl 0079.35406; Zbl 0032.17302

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