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.

中文翻译：

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协方差算子平滑函数的渐近有效估计

B\rangle$（而不是感兴趣的参数 $\langle f(\Sigma),B\rangle$），具有参数收敛率 $O(n^{-1/2})$，前提是有效秩 ${ \bf r}(\Sigma):= \frac{{\bf tr}(\Sigma)}{\|\Sigma\|}$ (${\rm tr}(\Sigma)$ 是轨迹和 $\ |\Sigma\|$ 是 $\Sigma$ 的算子范数) 满足假设 ${\bf r}(\Sigma)=o(n).$ 同时，我们证明这个估计量的偏差是通常与 $\frac{{\bf r}(\Sigma)}{n}$ 一样大（如果 ${\bf r}(\Sigma)\geq 大于 $n^{-1/2}$ n^{1/2}$）。在 ${\mathbb H}$ 是维度为 $d=o(n) 的有限维空间的情况下，我们开发了一种减少偏差的方法并构造了一个估计量 $\langle h(\hat \Sigma),B \rangle$ 的 $\lange f(\Sigma),B\rangle$ 是渐近正态的，收敛速度为 $O(n^{-1/2}).$ 此外，