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On an asymptotic optimality property of play-the-winner and vector-at-a-time sampling.

Elliott Nebenzahl (1984)

Trabajos de Estadística e Investigación Operativa

Simon and Weiss (1975) consider the formulation of the clinical trial as a selection procedure (Bechhofer, Kiefer and Sobel, 1968). The object of the trial is to choose the better treatment with probability ≥ P*, where P* is assigned, when the difference in success probabilities is ≥ Δ*, Δ* also being assigned. They consider a family of single step allocation methods for the reduction of the number of patients given the poorer treatment. Using numerical results, Simon and Weiss conclude that if...

On randomized stopping times.

Concepción Arenas Solá (1990)

Trabajos de Estadística

In this note we give a proof of the fact that the extremal elements of the set of randomized stopping times are exactly the stopping times.

Optimal sequential multiple hypothesis testing in presence of control variables

Andrey Novikov (2009)

Kybernetika

Suppose that at any stage of a statistical experiment a control variable X that affects the distribution of the observed data Y at this stage can be used. The distribution of Y depends on some unknown parameter θ , and we consider the problem of testing multiple hypotheses H 1 : θ = θ 1 , H 2 : θ = θ 2 , ... , H k : θ = θ k allowing the data to be controlled by X , in the following sequential context. The experiment starts with assigning a value X 1 to the control variable and observing Y 1 as a response. After some analysis, another value X 2 for...

Optimal sequential multiple hypothesis tests

Andrey Novikov (2009)

Kybernetika

This work deals with a general problem of testing multiple hypotheses about the distribution of a discrete-time stochastic process. Both the Bayesian and the conditional settings are considered. The structure of optimal sequential tests is characterized.

Optimal sequential procedures with Bayes decision rules

Andrey Novikov (2010)

Kybernetika

In this article, a general problem of sequential statistical inference for general discrete-time stochastic processes is considered. The problem is to minimize an average sample number given that Bayesian risk due to incorrect decision does not exceed some given bound. We characterize the form of optimal sequential stopping rules in this problem. In particular, we have a characterization of the form of optimal sequential decision procedures when the Bayesian risk includes both the loss due to incorrect...

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