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On sequential hypotheses testing via convex optimization
Anatoli Juditsky, Arkadi Nemirovski · 2014-12-04 · via math.ST updates on arXiv.org

We propose a new approach to sequential testing which is an adaptive (on-line) extension of the (off-line) framework developed in [10]. It relies upon testing of pairs of hypotheses in the case where each hypothesis states that the vector of parameters underlying the dis- tribution of observations belongs to a convex set. The nearly optimal under appropriate conditions test is yielded by a solution to an efficiently solvable convex optimization prob- lem. The proposed methodology can be seen as a computationally friendly reformulation of the classical sequential testing.

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