Handle URI:
http://hdl.handle.net/10754/599001
Title:
Nonparametric Inference for Periodic Sequences
Authors:
Sun, Ying; Hart, Jeffrey D.; Genton, Marc G.
Abstract:
This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.
Citation:
Sun Y, Hart JD, Genton MG (2012) Nonparametric Inference for Periodic Sequences. Technometrics 54: 83–96. Available: http://dx.doi.org/10.1080/00401706.2012.650499.
Publisher:
Informa UK Limited
Journal:
Technometrics
Issue Date:
Feb-2012
DOI:
10.1080/00401706.2012.650499
Type:
Article
ISSN:
0040-1706; 1537-2723
Sponsors:
Professor Hart's research was supported in part by NSF grant DMS-0604801. Professor Genton's research was partially supported by NSF (National Science Foundation) grant DMS1007504, and award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
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Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorHart, Jeffrey D.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T13:50:56Zen
dc.date.available2016-02-25T13:50:56Zen
dc.date.issued2012-02en
dc.identifier.citationSun Y, Hart JD, Genton MG (2012) Nonparametric Inference for Periodic Sequences. Technometrics 54: 83–96. Available: http://dx.doi.org/10.1080/00401706.2012.650499.en
dc.identifier.issn0040-1706en
dc.identifier.issn1537-2723en
dc.identifier.doi10.1080/00401706.2012.650499en
dc.identifier.urihttp://hdl.handle.net/10754/599001en
dc.description.abstractThis article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.en
dc.description.sponsorshipProfessor Hart's research was supported in part by NSF grant DMS-0604801. Professor Genton's research was partially supported by NSF (National Science Foundation) grant DMS1007504, and award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.en
dc.publisherInforma UK Limiteden
dc.subjectConsistencyen
dc.subjectCross-validationen
dc.subjectModel selectionen
dc.subjectNonparametric estimationen
dc.subjectPerioden
dc.titleNonparametric Inference for Periodic Sequencesen
dc.typeArticleen
dc.identifier.journalTechnometricsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
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