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Communication Dans Un Congrès Année : 2013

Nonparametric testing by convex optimization

Résumé

We discuss a general approach to handling a class of nonparametric detection problems when the null and each particular alternative hypothesis states that the vector of parameters identifying the distribution of observations belongs to a convex compact. Our central result is a test for a pair of hypotheses of the outlined type which, under appropriate assumptions, is provably nearly optimal. The test is yielded by a solution to a convex programming problem, and, as a result, the proposed construction admits a computationally e cient implementation. We show how our approach can be applied to a rather general detection problem encompassing several classical statistical settings such as detection of abrupt signal changes, cusp detection and multi-sensor detection.
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Dates et versions

hal-00981883 , version 1 (23-04-2014)

Identifiants

  • HAL Id : hal-00981883 , version 1

Citer

Anatoli B. Juditsky, Alexander Goldenshluger, Arkadii S. Nemirovski. Nonparametric testing by convex optimization. WSL 2013 - International Workshop on Statistical Learning, Jun 2013, Moscow, Russia. ⟨hal-00981883⟩
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