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Improved nonparametric inference for multiple correlated periodic sequences
Author(s) -
Sun Ying,
Hart Jeffrey D.,
Genton Marc G.
Publication year - 2013
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.28
Subject(s) - akaike information criterion , inference , statistics , nonparametric statistics , multivariate statistics , mathematics , computer science , artificial intelligence
This paper proposes a cross‐validation method for estimating the period as well as the values of multiple correlated periodic sequences when data are observed at evenly spaced time points. The period of interest is estimated conditional on the other correlated sequences. An alternative method for period estimation based on Akaike's information criterion is also discussed. The improvement of the period estimation performance is investigated both theoretically and by simulation. We apply the multivariate cross‐validation method to the temperature data obtained from multiple ice cores, investigating the periodicity of the El Niño effect. Our methodology is also illustrated by estimating patients’ cardiac cycle from different physiological signals, including arterial blood pressure, electrocardiography, and fingertip plethysmograph. Copyright © 2013 John Wiley & Sons Ltd