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april 1999 Minimax nonparametric hypothesis testing: the case of an inhomogeneous alternative
Oleg V. Lepski, Vladimir G. Spokoiny
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Bernoulli 5(2): 333-358 (april 1999).

Abstract

We study the problem of testing a simple hypothesis for a nonparametric ''signal + white-noise'' model. It is assumed under the null hypothesis that the ''signal'' is completely specified, e.g., that no signal is present. This hypothesis is tested against a composite alternative of the following form: the underlying function (the signal) is separated away from the null in the L2 norm and, in addition, it possesses some smoothness properties. We focus on the case of an inhomogeneous alternative when the smoothness properties of the signal are measured in an Lp norm with p<2. We consider tests whose errors have probabilities which do not exceed prescribed values and we measure the quality of testing by the minimal distance between the null and the alternative set for which such testing is still possible. We evaluate the optimal rate of decay for this distance to zero as the noise level tends to zero. Then a rate-optimal test is proposed which essentially uses a pointwise-adaptive estimation procedure.

Citation

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Oleg V. Lepski. Vladimir G. Spokoiny. "Minimax nonparametric hypothesis testing: the case of an inhomogeneous alternative." Bernoulli 5 (2) 333 - 358, april 1999.

Information

Published: april 1999
First available in Project Euclid: 5 March 2007

zbMATH: 0946.62050
MathSciNet: MR1681702

Keywords: Bandwidth selection , error probabilities , minimax hypothesis testing , nonparametric alternative , pointwise adaptive estimation , signal detection

Rights: Copyright © 1999 Bernoulli Society for Mathematical Statistics and Probability

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Vol.5 • No. 2 • april 1999
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