Near-Optimal and Tractable Estimation under Shift-Invariance

Abstract

How hard is it to estimate a discrete-time signal $(x_{1}, ..., x_{n}) \in \mathbb{C}^n$ satisfying an unknown linear recurrence relation of order $s$ and observed in i.i.d. complex Gaussian noise? The class of all such signals is parametric but extremely rich: it contains all exponential polynomials over $\mathbb{C}$ with total degree $s$, including harmonic oscillations with $s$ arbitrary frequencies. Geometrically, this class corresponds to the projection onto $\mathbb{C}^{n}$ of the union of all shift-invariant subspaces of $\smash{\mathbb{C}^\mathbb{Z}}$ of dimension $s$. We show that the statistical complexity of this class, as measured by the squared minimax radius of the $(1-δ)$-confidence $\ell_2$-ball, is nearly the same as for the class of $s$-sparse signals, namely $\smash{O\left(s\log(en) + \log(δ^{-1})\right) \cdot \log^2(es) \cdot \log(en/s).}$ Moreover, the corresponding near-minimax estimator is tractable, and it can be used to build a test statistic with a near-minimax detection threshold in the associated detection problem. These statistical results rely upon a simple analytic observation: the interpretation of the Fourier coefficients of the Christoffel function of any shift-invariant subspace of $\smash{\mathbb{C}^\mathbb{Z}}$ as a reproducing filter with the smallest possible spectrum in all $\ell_p$-norms, $p \in [1,\infty]$, at once.