2011arXiv (Cornell University)Open access

Minimax image detection from noisy tomographic data

Yuri I. Ingster, Theofanis Sapatinas, Irina A. Suslina

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Abstract

We consider the detection problem of a two-dimensional function from noisy observations of its integrals over lines. We study both rate and sharp asymptotics for the error probabilities in the minimax setup. By construction, the derived tests are non-adaptive. We also construct a minimax rate-optimal adaptive test of rather simple structure.

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What this paper is about

We consider the detection problem of a two-dimensional function from noisy observations of its integrals over lines. We study both rate and sharp asymptotics for the error probabilities in the minimax setup. By construction, the derived tests are non-adaptive. We also construct a minimax rate-optimal adaptive test of rather simple structure.

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Available abstract

We consider the detection problem of a two-dimensional function from noisy observations of its integrals over lines. We study both rate and sharp asymptotics for the error probabilities in the minimax setup. By construction, the derived tests are non-adaptive. We also construct a minimax rate-optimal adaptive test of rather simple structure.

Key concepts: Minimax, Simple (philosophy), Mathematics, Function (biology), Construct (python library), Image (mathematics), Minimax approximation algorithm, Algorithm

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