Optimal Linear Functional Estimation for Proportionally High-Dimensional Linear Models

Abstract

We study estimation of a linear functional in random-design linear models when $\gamma=p/n \in (1, \infty)$ under the dense regime where the design covariance is unknown, and neither the regression vector nor the precision matrix is assumed sparse. Suppose the spectrum of the design covariance belongs to $[\ell,L]$, we establish the minimax mean squared rate for the linear functional has the order $n^{-\alpha(\kappa,\gamma)}$, where the exponent $\alpha(\kappa,\gamma)$ has an explicit elliptic-integral representation and $\alpha(\kappa,\gamma)<1$ when both $\kappa>1$ and $\gamma>1$. We propose the Bocca estimator, which combines empirically calibrated resolvents through a complex contour integral to attain the optimal rate.