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Composite Quantile Periodogram for Spectral Analysis

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  • Yaeji Lim
  • Hee-Seok Oh

Abstract

type="main" xml:id="jtsa12143-abs-0001"> We propose a new type of periodogram for identifying hidden frequencies and providing a better understanding of the frequency behaviour. The quantile periodogram by Li ( ) provides richer information on the frequency of signal than a single estimation of the mean frequency does. However, it is difficult to find a specific quantile that identifies hidden frequencies. In this study, we consider a weighted linear combination of quantile periodograms, termed 'composite quantile periodogram'. It is completely data adaptive and does not require prior knowledge of the signal. Simulation results and real-data example demonstrate significant improvement in the quality of the periodogram.

Suggested Citation

  • Yaeji Lim & Hee-Seok Oh, 2016. "Composite Quantile Periodogram for Spectral Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 195-221, March.
    • Handle: RePEc:bla:jtsera:v:37:y:2016:i:2:p:195-221
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    File URL: http://hdl.handle.net/10.1111/jtsa.12143
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    References listed on IDEAS

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    1. Chen, Tianbo & Sun, Ying & Li, Ta-Hsin, 2021. "A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).

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