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- W1673161806 abstract "We focus on one-sided, mixture-based stopping rules for the problem of sequential testing a simple null hypothesis against a composite alternative. For the latter, we consider two cases---either a discrete alternative or a continuous alternative that can be embedded into an exponential family. For each case, we find a mixture-based stopping rule that is nearly minimax in the sense of minimizing the maximal Kullback-Leibler information. The proof of this result is based on finding an almost Bayes rule for an appropriate sequential decision problem and on high-order asymptotic approximations for the performance characteristics of arbitrary mixture-based stopping times. We also evaluate the asymptotic performance loss of certain intuitive mixture rules and verify the accuracy of our asymptotic approximations with simulation experiments." @default.
- W1673161806 created "2016-06-24" @default.
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- W1673161806 date "2011-10-05" @default.
- W1673161806 modified "2023-09-27" @default.
- W1673161806 title "Nearly Minimax One-Sided Mixture-Based Sequential Tests" @default.
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