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Functional linear regression: dependence and error contamination

Chen, Cheng, Guo, Shaojun and Qiao, Xinghao ORCID: 0000-0002-6546-6595 (2020) Functional linear regression: dependence and error contamination. Journal of Business and Economic Statistics. ISSN 0735-0015

[img] Text (Functional Linear Regression. Dependence and Error) - Accepted Version
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Identification Number: 10.1080/07350015.2020.1832503


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 iid 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 article, 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.

Item Type: Article
Official URL:
Additional Information: © 2020 American Statistical Association
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 08 Apr 2022 15:12
Last Modified: 11 Apr 2022 09:12

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