Sharp variable selection of a sparse submatrix in a high-dimensional noisy matrix

Butucea, Cristina; Ingster, Yuri I.; Suslina, Irina A.

ESAIM: Probability and Statistics, Tome 19 (2015), pp. 115-134.

We observe a $N\times M$ matrix of independent, identically distributed Gaussian random variables which are centered except for elements of some submatrix of size $n\times m$ where the mean is larger than some $a>0$. The submatrix is sparse in the sense that $n/N$ and $m/M$ tend to 0, whereas $n,m,N$ and $M$ tend to infinity. We consider the problem of selecting the random variables with significantly large mean values, as was also considered by [M. Kolar, S. Balakrishnan, A. Rinaldo and A. Singh, NIPS (2011)]. We give sufficient conditions on $a$ as a function of $n,m,N$ and $M$ and construct a uniformly consistent procedure in order to do sharp variable selection. We also prove the minimax lower bounds under necessary conditions which are complementary to the previous conditions. The critical values $a^{*}$ separating the necessary and sufficient conditions are sharp (we show exact constants), whereas [M. Kolar, S. Balakrishnan, A. Rinaldo and A. Singh, NIPS (2011)] only prove rate optimality and focus on suboptimal computationally feasible selectors. Note that rate optimality in this problem leaves out a large set of possible parameters, where we do not know whether consistent selection is possible.

Classification : 62G05, 62G20
Mots clés : Estimation, minimax testing, large matrices, selection of sparse signal, sharp selection bounds, variable selection

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