Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function

Details Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function

2005-04-25

arxiv.org/abs/math/0504509v1

Annals of Statistics 2005, Vol. 33, No. 1, 214-257

Mathematics

Statistics Theory

Published at http://dx.doi.org/10.1214/009053604000000896 in the Annals of Statistics (http://www.imstat.org/aos/) by the Inst

Scientific paper

10.1214/009053604000000896

In this paper we propose a general methodology, based on multiple testing, for testing that the mean of a Gaussian vector in R^n belongs to a convex set. We show that the test achieves its nominal level, and characterize a class of vectors over which the tests achieve a prescribed power. In the functional regression model this general methodology is applied to test some qualitative hypotheses on the regression function. For example, we test that the regression function is positive, increasing, convex, or more generally, satisfies a differential inequality. Uniform separation rates over classes of smooth functions are established and a comparison with other results in the literature is provided. A simulation study evaluates some of the procedures for testing monotonicity.

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