Banff International Research Station for Mathematical Innovation and Discovery
Litvak, Alexander
—
2017-11-22
Let $X$ be an $n$-dimensional random centered Gaussian vector with independent but not necessarily identically distributed coordinates and let $T$ be an orthogonal transformation of $\R^n$. We show that the random vector $Y=T(X)$ satisfies
$$
\mathbb{E} \sum \limits_{j=1}^k j\mobx{-}\min _{i\leq n}{X_{i}}^2 \leq C \mathbb{E} \sum\limits_{j=1}^k j\mobx{-}\min _{i\leq n}{Y_{i}}^2
$$
for all $k\leq n$, where ``$\jm$'' denotes the $j$-th smallest component of the corresponding vector
and $C>0$ is a universal constant. This resolves (up to a multiplicative constant) an old question
of S.Mallat and O.Zeitouni regarding optimality of the Karhunen--Lo\`eve basis for the nonlinear
reconstruction. We also show some relations for order statistics of random vectors
(not only Gaussian) which are of independent interest. This is a joint work with Konstantin Tikhomirov.
Non UBC
Unreviewed
Author affiliation: University of Alberta
Faculty
http://creativecommons.org/licenses/by-nc-nd/4.0/