Efficient Estimation of Linear Functionals of Principal Components

TL;DR

This paper introduces a bias-reduced estimation method for linear functionals of principal components in high-dimensional Gaussian settings, achieving asymptotic normality and optimality under certain rank conditions.

Contribution

It develops a novel bias reduction technique for estimating linear functionals of eigenvectors in PCA within infinite-dimensional spaces, establishing asymptotic properties and minimax optimality.

Findings

Establishes asymptotic normality of the estimators.

Proves minimax lower bounds matching the estimators' risk.

Demonstrates semi-parametric optimality under effective rank conditions.

Abstract

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.