Adaptive estimation of the sparsity in the Gaussian vector model

Alexandra Carpentier; Nicolas Verzelen

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arXiv · 1703.00167

Adaptive estimation of the sparsity in the Gaussian vector model

Alexandra Carpentier·Nicolas Verzelen

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Abstract

Consider the Gaussian vector model with mean value {\theta}. We study the twin problems of estimating the number |{\theta}|_0 of non-zero components of {\theta} and testing whether |{\theta}|_0 is smaller than some value. For testing, we establish the minimax separation distances for this model and introduce a minimax adaptive test. Extensions to the case of unknown variance are also discussed. Rewriting the estimation of |{\theta}|_0 as a multiple testing problem of all hypotheses {|{\theta}|_0 <= q}, we both derive a new way of assessing the optimality of a sparsity estimator and we exhibit such an optimal procedure. This general approach provides a roadmap for estimating the complexity of the signal in various statistical models.

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BibTeX RIS

Alexandra Carpentier, Nicolas Verzelen. 2017-03-01. Adaptive estimation of the sparsity in the Gaussian vector model. https://arxiv.org/abs/1703.00167

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