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Uniform Convergence of Compactly Supported Wavelet Expansions of Gaussian Random Processes

Author

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  • Yuriy Kozachenko
  • Andriy Olenko
  • Olga Polosmak

Abstract

New results on uniform convergence in probability for expansions of Gaussian random processes using compactly supported wavelets are given. The main result is valid for general classes of non stationary processes. An application of the obtained results to stationary processes is also presented. It is shown that the convergence rate of the expansions is exponential.

Suggested Citation

  • Yuriy Kozachenko & Andriy Olenko & Olga Polosmak, 2014. "Uniform Convergence of Compactly Supported Wavelet Expansions of Gaussian Random Processes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(10-12), pages 2549-2562, May.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:10-12:p:2549-2562
    DOI: 10.1080/03610926.2013.784338
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    Cited by:

    1. Turchyn Ievgen, 2019. "Wavelet-based simulation of random processes from certain classes with given accuracy and reliability," Monte Carlo Methods and Applications, De Gruyter, vol. 25(3), pages 217-225, September.

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