Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive estimation of the sparsity in the Gaussian vector model   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.

36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci