Semiparametric shape-invariant models for periodic data

Holger Hurtgen and Daniel Gervini

Journal of Applied Statistics, 2009, vol. 36, issue 10, 1055-1065

Abstract: This article presents a novel shape-invariant modeling approach to quasi-periodic data. We propose a dynamic semiparametric method that estimates the common cycle shape in a nonparametric way and the individual phase and amplitude variability in a parametric way. An efficient algorithm to compute the estimators is proposed. The behavior of the estimators is studied by simulation and by a real-data example.

Keywords: circadian rhythms; nonparametric regression; spline smoothing (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1080/02664760802562472

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