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Wavelet Estimation of Nonstationary Spatial Covariance Function

In: Time Series and Wavelet Analysis

Author

Listed:
  • Yangyang Chen

    (University of São Paulo, Department of Statistics, Institute of Mathematics and Statistics)

  • Pedro A. Morettin

    (University of São Paulo, Department of Statistics, Institute of Mathematics and Statistics)

  • Ronaldo Dias

    (University of Campinas, Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing)

  • Chang Chiann

    (University of São Paulo, Department of Statistics, Institute of Mathematics and Statistics)

Abstract

This work proposes a new procedure for estimating the non-stationary spatial covariance function for spatio-temporal deformation. The proposed procedure is based on a monotonic function approach. The spatio-temporal deformation functions are expanded as a linear combination of wavelet bases. The estimate of the deformation guarantees an injective transformation, such that two distinct locations in the geographic plane are not mapped into the same point in the deformation plane. Simulation studies have shown the effectiveness of this procedure. An application to historical daily maximum temperature records exemplifies the flexibility of the proposed methodology when dealing with real datasets.

Suggested Citation

  • Yangyang Chen & Pedro A. Morettin & Ronaldo Dias & Chang Chiann, 2024. "Wavelet Estimation of Nonstationary Spatial Covariance Function," Springer Books, in: Chang Chiann & Aluisio de Souza Pinheiro & Clélia Maria Castro Toloi (ed.), Time Series and Wavelet Analysis, pages 281-293, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-66398-7_15
    DOI: 10.1007/978-3-031-66398-7_15
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