Semiparametric shape-invariant models for periodic data

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.

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Year of publication:	2009
Authors:	Hurtgen, Holger ; Gervini, Daniel
Published in:	Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 36.2009, 10, p. 1055-1065
Publisher:	Taylor & Francis Journals
Subject:	circadian rhythms \| nonparametric regression \| spline smoothing

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Extent:	text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008466706