Nonparametric Inference for Periodic Sequences

Ying Sun*, Jeffrey D. Hart, Marc G. Genton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)83-96
Number of pages14
JournalTechnometrics
Volume54
Issue number1
DOIs
StatePublished - Feb 2012
Externally publishedYes

Fingerprint

Dive into the research topics of 'Nonparametric Inference for Periodic Sequences'. Together they form a unique fingerprint.

Cite this