  We study the problem of adaptive variable selection in a Gaussian white noise
model of intensity $\varepsilon$ under certain sparsity and regularity
conditions on an unknown regression function $f$. The $d$-variate regression
function $f$ is assumed to be a sum of functions each depending on a smaller
number $k$ of variables ($1 \leq k \leq d$). These functions are unknown to us
and only few of them are nonzero. We assume that $d=d_\varepsilon \to \infty$
as $\varepsilon \to 0$ and consider the cases when $k$ is fixed and when
$k=k_\varepsilon \to \infty$, $k=o(d)$ as $\varepsilon \to 0$. In this work, we
introduce an adaptive selection procedure that, under some model assumptions,
identifies exactly all nonzero $k$-variate components of $f$. In addition, we
establish conditions under which exact identification of the nonzero components
is impossible. These conditions ensure that the proposed selection procedure is
the best possible in the asymptotically minimax sense with respect to the
Hamming risk.
