Title: Asymptotically Efficient Estimation of Smooth Functionals of Covariance Operators,
Abstract: Let $X$ be a centered Gaussian random variable in a separable Hilbert space
${\mathbb H}$ with covariance operator $\Sigma.$ We study a problem of
estimation of a smooth functional of $\Sigma$ based on a sample $X_1,\dots
,X_n$ of $n$ independent observations of $X.$ More specifically, we are
interested in functionals of the form $\langle f(\Sigma), B\rangle,$ where
$f:{\mathbb R}\mapsto {\mathbb R}$ is a smooth function and $B$ is a nuclear
operator in ${\mathbb H}.$ We prove concentration and normal approximation
bounds for plug-in estimator $\langle f(\hat \Sigma),B\rangle,$ $\hat
\Sigma:=n^{-1}\sum_{j=1}^n X_j\otimes X_j$ being the sample covariance based on
$X_1,\dots, X_n.$ These bounds show that $\langle f(\hat \Sigma),B\rangle$ is
an asymptotically normal estimator of its expectation ${\mathbb E}_{\Sigma}
\langle f(\hat \Sigma),B\rangle$ (rather than of parameter of interest $\langle
f(\Sigma),B\rangle$) with a parametric convergence rate $O(n^{-1/2})$ provided
that the effective rank ${\bf r}(\Sigma):= \frac{{\bf tr}(\Sigma)}{\|\Sigma\|}$
(${\rm tr}(\Sigma)$ being the trace and $\|\Sigma\|$ being the operator norm of
$\Sigma$) satisfies the assumption ${\bf r}(\Sigma)=o(n).$ At the same time, we
show that the bias of this estimator is typically as large as $\frac{{\bf
r}(\Sigma)}{n}$ (which is larger than $n^{-1/2}$ if ${\bf r}(\Sigma)\geq
n^{1/2}$). In the case when ${\mathbb H}$ is finite-dimensional space of
dimension $d=o(n),$ we develop a method of bias reduction and construct an
estimator $\langle h(\hat \Sigma),B\rangle$ of $\langle f(\Sigma),B\rangle$
that is asymptotically normal with convergence rate $O(n^{-1/2}).$ Moreover, we
study asymptotic properties of the risk of this estimator and prove minimax
lower bounds for arbitrary estimators showing the asymptotic efficiency of
$\langle h(\hat \Sigma),B\rangle$ in a semi-parametric sense.