Minimax Estimation of Partially-Observed Vector AutoRegressions

TL;DR

This paper develops a near-optimal estimator for sparse transition matrices in partially-observed vector autoregressive models, analyzing how sampling, noise, and sparsity affect estimation accuracy through theoretical bounds and simulations.

Contribution

It introduces a Yule-Walker and Dantzig selector-based estimator for partially observed VARs and establishes its near-optimal error bounds with insights into key influencing parameters.

Findings

Estimator achieves near-minimax optimal error bounds.

Sampling ratio, noise level, and sparsity significantly impact estimation accuracy.

Numerical experiments validate theoretical predictions.

Abstract

High-dimensional time series are a core ingredient of the statistical modeling toolkit, for which numerous estimation methods are known.But when observations are scarce or corrupted, the learning task becomes much harder.The question is: how much harder? In this paper, we study the properties of a partially-observed Vector AutoRegressive process, which is a state-space model endowed with a stochastic observation mechanism.Our goal is to estimate its sparse transition matrix, but we only have access to a small and noisy subsample of the state components.Interestingly, the sampling process itself is random and can exhibit temporal correlations, a feature shared by many realistic data acquisition scenarios.We start by describing an estimator based on the Yule-Walker equation and the Dantzig selector, and we give an upper bound on its non-asymptotic error.Then, we provide a matching minimax lower bound, thus proving near-optimality of our estimator.The convergence rate we obtain sheds light on the role of several key parameters such as the sampling ratio, the amount of noise and the number of non-zero coefficients in the transition matrix.These theoretical findings are commented and illustrated by numerical experiments on simulated data.