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Title: Sign tests for weak principal directions Authors:  Davy Paindaveine - Universite libre de Bruxelles (Belgium)
Julien Remy - Universite libre de Bruxelles (Belgium)
Thomas Verdebout - Universite Libre de Bruxelles (Belgium)
Julien Remy - Université Libre de Bruxelles (Belgium) [presenting]
Abstract: Inference on the first principal direction of a $p$-variate elliptical distribution is considered. We do so in challenging double asymptotic scenarios for which this direction eventually fails to be identifiable. In order to achieve robustness not only with respect to such weak identifiability but also with respect to heavy tails, we focus on sign-based statistical procedures, that is, on procedures that involve the observations only through their direction from the center of the distribution. We first focus on weak identifiability setups involving single spikes. We show that, irrespective of the degree of weak identifiability, such setups offer local alternatives for which the corresponding sequence of statistical experiments converges in the Le Cam sense. We exploit this convergence result to build optimal sign tests for the problem considered. In classical asymptotic scenarios where the spectrum is fixed, these tests are shown to be asymptotically equivalent to the sign-based likelihood ratio tests available in the literature. Unlike the latter, however, the proposed sign tests are robust to arbitrarily weak identifiability. We show that our tests meet the asymptotic level constraint irrespective of the structure of the spectrum, hence also in possibly multi-spike setups. We fully characterize the non-null asymptotic distributions of the corresponding test statistics under weak identifiability, which allows us to quantify the corresponding local asymptotic powers.