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April 2013 Fourier analysis of stationary time series in function space
Victor M. Panaretos, Shahin Tavakoli
Ann. Statist. 41(2): 568-603 (April 2013). DOI: 10.1214/13-AOS1086

Abstract

We develop the basic building blocks of a frequency domain framework for drawing statistical inferences on the second-order structure of a stationary sequence of functional data. The key element in such a context is the spectral density operator, which generalises the notion of a spectral density matrix to the functional setting, and characterises the second-order dynamics of the process. Our main tool is the functional Discrete Fourier Transform (fDFT). We derive an asymptotic Gaussian representation of the fDFT, thus allowing the transformation of the original collection of dependent random functions into a collection of approximately independent complex-valued Gaussian random functions. Our results are then employed in order to construct estimators of the spectral density operator based on smoothed versions of the periodogram kernel, the functional generalisation of the periodogram matrix. The consistency and asymptotic law of these estimators are studied in detail. As immediate consequences, we obtain central limit theorems for the mean and the long-run covariance operator of a stationary functional time series. Our results do not depend on structural modelling assumptions, but only functional versions of classical cumulant mixing conditions, and are shown to be stable under discrete observation of the individual curves.

Citation

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Victor M. Panaretos. Shahin Tavakoli. "Fourier analysis of stationary time series in function space." Ann. Statist. 41 (2) 568 - 603, April 2013. https://doi.org/10.1214/13-AOS1086

Information

Published: April 2013
First available in Project Euclid: 26 April 2013

zbMATH: 1267.62094
MathSciNet: MR3099114
Digital Object Identifier: 10.1214/13-AOS1086

Subjects:
Primary: 62M10
Secondary: 60G10, 62M15

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.41 • No. 2 • April 2013
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