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.
Categories: math.ST stat.TH