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Quelques contributions à la sélection de variables et aux tests non-paramétriques

Laëtitia Comminges 1 
1 imagine [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : Real-world data are often extremely high-dimensional, severely under constrained and interspersed with a large number of irrelevant or redundant features. Relevant variable selection is a compelling approach for addressing statistical issues in the scenario of high-dimensional and noisy data with small sample size. First, we address the issue of variable selection in the regression model when the number of variables is very large. The main focus is on the situation where the number of relevant variables is much smaller than the ambient dimension. Without assuming any parametric form of the underlying regression function, we get tight conditions making it possible to consistently estimate the set of relevant variables. Secondly, we consider the problem of testing a particular type of composite null hypothesis under a nonparametric multivariate regression model. For a given quadratic functional Q, the null hypothesis states that the regression function f satisfies the constraint Q[f] = 0, while the alternative corresponds to the functions for which Q[f] is bounded away from zero. We provide minimax rates of testing and the exact separation constants, along with a sharp-optimal testing procedure, for diagonal and nonnegative quadratic functionals. We can apply this to testing the relevance of a variable. Studying minimax rates for quadratic functionals which are neither positive nor negative, makes appear two different regimes: “regular” and “irregular”. We apply this to the issue of testing the equality of norms of two functions observed in noisy environments
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Submitted on : Tuesday, March 26, 2013 - 5:12:09 PM
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Laëtitia Comminges. Quelques contributions à la sélection de variables et aux tests non-paramétriques. Mathématiques générales [math.GM]. Université Paris-Est, 2012. Français. ⟨NNT : 2012PEST1068⟩. ⟨pastel-00804979⟩



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