Functional Linear Regression: Dependence and Error Contamination

TL;DR

This paper introduces a new autocovariance-based estimation method for functional linear regression that accounts for serial dependence and nonparametric noise covariance, improving accuracy over existing methods.

Contribution

It proposes a novel autocovariance-based generalized method-of-moments estimator for the slope function in dependent functional data with nonparametric noise structure.

Findings

Estimator outperforms competing methods in simulations

Method effectively handles serial dependence in functional predictors

Approach is validated on a public financial dataset

Abstract

Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an underlying stochastic process, and are observed over a grid of points contaminated by i.i.d. measurement errors. In practice, however, the dynamical dependence across different curves may exist and the parametric assumption on the error covariance structure could be unrealistic. In this paper, we consider functional linear regression with serially dependent observations of the functional predictor, when the contamination of the predictor by the white noise is genuinely functional with fully nonparametric covariance structure. Inspired by the fact that the autocovariance function of observed functional predictors automatically filters out the impact from the unobservable noise term, we propose a novel autocovariance-based generalized method-of-moments estimate of the slope function. We also develop a nonparametric smoothing approach to handle the scenario of partially observed functional predictors. The asymptotic properties of the resulting estimators under different scenarios are established. Finally, we demonstrate that our proposed method significantly outperforms possible competing methods through an extensive set of simulations and an analysis of a public financial dataset.