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Using the periodogram to estimate period in nonparametric regression

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  • Peter Hall
  • Ming Li

Abstract

Properties of the periodogram are seldom studied in the setting of nonparametric regression, although that is the context in which the periodogram is widely applied in astronomy. There it is a competitor with more recent least-squares methods. The periodogram has the advantage of providing significant graphical insight into statistical and numerical aspects of the problem. However, as we show in the present paper, it also has drawbacks. The estimator that it produces has somewhat higher variance than its least-squares counterpart, and a periodogram-based approach is more prone to suffer difficulties caused by periodicity of the observation schedule. While the periodogram remains a very attractive tool, the information provided in this paper allows users to assess more readily the extent to which it can be relied upon in a nonparametric setting. This aspect of our contributions is discussed theoretically and illustrated by numerical studies involving a real dataset. Copyright 2006, Oxford University Press.

Suggested Citation

  • Peter Hall & Ming Li, 2006. "Using the periodogram to estimate period in nonparametric regression," Biometrika, Biometrika Trust, vol. 93(2), pages 411-424, June.
  • Handle: RePEc:oup:biomet:v:93:y:2006:i:2:p:411-424
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    File URL: http://hdl.handle.net/10.1093/biomet/93.2.411
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    References listed on IDEAS

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    Cited by:

    1. Li, Ming, 2017. "Record length requirement of long-range dependent teletraffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 164-187.
    2. Michael Vogt & Oliver Linton, 2014. "Nonparametric estimation of a periodic sequence in the presence of a smooth trend," Biometrika, Biometrika Trust, vol. 101(1), pages 121-140.
    3. repec:eee:phsmap:v:484:y:2017:i:c:p:309-335 is not listed on IDEAS
    4. Thieler, Anita M. & Fried, Roland & Rathjens, Jonathan, 2016. "RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i09).

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