We study principal component analysis (PCA) for mean zero i.i.d. Gaussian observations $X_1,\dots, X_n$ in a separable Hilbert space $\mathbb{ H}$ with unknown covariance operator $\Sigma.$ The complexity of the problem is characterized by its effective rank ${ \bf r}(\Sigma):= \frac{ { \rm tr}(\Sigma)}{ \|\Sigma\|},$ where ${ \rm tr}(\Sigma)$ denotes the trace of $\Sigma$ and $\|\Sigma\|$ denotes its operator norm. We develop a method of bias reduction in the problem of estimation of linear functionals of eigenvectors of $\Sigma.$ Under the assumption that ${ \bf r}(\Sigma)=o(n),$ we establish the asymptotic normality and asymptotic properties of the risk of the resulting estimators and prove matching minimax lower bounds, showing their semi-parametric optimality.
Categories: math.ST stat.TH