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Warped functional regression

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  • Daniel Gervini

Abstract

A characteristic feature of functional data is the presence of phase variability in addition to amplitude variability. Existing functional regression methods do not handle time variability in an explicit and efficient way. In this paper we introduce a functional regression method that incorporates time warping as an intrinsic part of the model. The method achieves good predictive power in a parsimonious way and allows unified statistical inference about phase and amplitude components. The asymptotic distribution of the estimators is derived and their finite-sample properties are studied by simulation. An application involving ground-level ozone trajectories is presented.

Suggested Citation

  • Daniel Gervini, 2015. "Warped functional regression," Biometrika, Biometrika Trust, vol. 102(1), pages 1-14.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:1-14.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu054
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    Cited by:

    1. Cody Carroll & Hans‐Georg Müller, 2023. "Latent deformation models for multivariate functional data and time‐warping separability," Biometrics, The International Biometric Society, vol. 79(4), pages 3345-3358, December.
    2. Ma, Yijia & Zhou, Xinyu & Wu, Wei, 2024. "A stochastic process representation for time warping functions," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).

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