Improved nonparametric inference for multiple correlated periodic sequences

Handle URI:
http://hdl.handle.net/10754/594060
Title:
Improved nonparametric inference for multiple correlated periodic sequences
Authors:
Sun, Ying; Hart, Jeffrey D.; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Sun Y, Hart JD, Genton MG (2013) Improved nonparametric inference for multiple correlated periodic sequences. Stat 2: 197–210. Available: http://dx.doi.org/10.1002/sta4.28.
Publisher:
Wiley-Blackwell
Journal:
Stat
Issue Date:
26-Aug-2013
DOI:
10.1002/sta4.28
Type:
Article
ISSN:
2049-1573
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorHart, Jeffrey D.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-01-19T13:20:31Zen
dc.date.available2016-01-19T13:20:31Zen
dc.date.issued2013-08-26en
dc.identifier.citationSun Y, Hart JD, Genton MG (2013) Improved nonparametric inference for multiple correlated periodic sequences. Stat 2: 197–210. Available: http://dx.doi.org/10.1002/sta4.28.en
dc.identifier.issn2049-1573en
dc.identifier.doi10.1002/sta4.28en
dc.identifier.urihttp://hdl.handle.net/10754/594060en
dc.description.abstractThis 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.en
dc.publisherWiley-Blackwellen
dc.subjectCross-validationen
dc.subjectModel selectionen
dc.subjectMultiple sequencesen
dc.subjectNonparametric estimationen
dc.subjectPerioden
dc.titleImproved nonparametric inference for multiple correlated periodic sequencesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalStaten
dc.contributor.institutionDepartment of Statistics; University of Chicago; Chicago IL 60637 USAen
dc.contributor.institutionDepartment of Statistics; Texas A&M University; College Station TX 77843 USAen
kaust.authorGenton, Marc G.en
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