On sequential hypotheses testing via convex optimization

On sequential hypotheses testing via convex optimization

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Article ID: iaor201526051
Volume: 76
Issue: 5
Start Page Number: 809
End Page Number: 825
Publication Date: May 2015
Journal: Automation and Remote Control
Authors: ,
Keywords: optimization, testing, programming: convex
Abstract:

We propose a new approach to sequential testing which is an adaptive (on‐line) extension of the (off‐line) framework developed in [1]. It relies upon testing of pairs of hypotheses in the case where each hypothesis states that the vector of parameters underlying the distribution 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 problem. The proposed methodology can be seen as a computationally friendly reformulation of the classical sequential testing.

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