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Multivariate Walsh‐Fourier Analysis

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  • David S. Stoffer

Abstract

. In this paper we establish a statistical methodology for the spectral analysis of stationary multivariate time series via the Walsh‐Fourier transform. Theoretical results pertaining to the definition and estimation of the Walsh‐Fourier spectral matrix and functions of that matrix including cross‐spectra, coherency and phase are given. An example of the statistical techniques developed in this paper is given; in particular, the methodologies are applied to neonatal sleep data collected from a study of the effect of maternal substance use during pregnancy.

Suggested Citation

  • David S. Stoffer, 1990. "Multivariate Walsh‐Fourier Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(1), pages 57-73, January.
    • Handle: RePEc:bla:jtsera:v:11:y:1990:i:1:p:57-73
    DOI: 10.1111/j.1467-9892.1990.tb00042.x
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    Cited by:

    1. E. Bajalinov & Sz. Duleba, 2020. "Seasonal time series forecasting by the Walsh-transformation based technique," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 983-1001, September.

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