Random Surface Covariance Estimation by Shifted Partial Tracing

TL;DR

This paper introduces a novel shifted partial tracing method for efficiently estimating non-separable covariance structures in surface-valued processes, even with noisy data, improving accuracy and computational speed over traditional separable models.

Contribution

The paper proposes a new non-parametric estimator based on shifted partial tracing that handles non-separable covariance components efficiently and accurately in noisy, dense observation settings.

Findings

Estimator is consistent under noise

Achieves similar computational cost as separable models

Demonstrated effectiveness in simulations and real data

Abstract

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even though the assumption may fail in practice. We consider a setting where the covariance structure may fail to be separable locally -- either due to noise contamination or due to the presence of a~non-separable short-range dependent signal component. That is, the covariance is an additive perturbation of a separable component by a~non-separable but banded component. We introduce non-parametric estimators hinging on the novel concept of shifted partial tracing, enabling computationally efficient estimation of the model under dense observation. Due to the denoising properties of shifted partial tracing, our methods are shown to yield consistent estimators even under noisy discrete observation, without the need for smoothing. Further to deriving the convergence rates and limit theorems, we also show that the implementation of our estimators, including prediction, comes at no computational overhead relative to a separable model. Finally, we demonstrate empirical performance and computational feasibility of our methods in an extensive simulation study and on a real data set.