ON MULTIVARIATE NON-GAUSSIAN SCALE INVARIANCE: FRACTIONAL LÉVY PROCESSES AND WAVELET ESTIMATION - Université Claude Bernard Lyon 1 Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

ON MULTIVARIATE NON-GAUSSIAN SCALE INVARIANCE: FRACTIONAL LÉVY PROCESSES AND WAVELET ESTIMATION

Résumé

In the modern world of "Big Data," dynamic signals are often multivariate and characterized by joint scale-free dynamics (self-similarity) and non-Gaussianity. In this paper, we examine the performance of joint wavelet eigenanalysis estimation for the Hurst parameters (scaling exponents) of non-Gaussian multivariate processes. We propose a new process called operator fractional Lévy motion (ofLm) as a Lévy-type model for non-Gaussian multivariate self-similarity. Based on large size Monte Carlo simulations of bivariate ofLm with a combination of Gaussian and non-Gaussian marginals, the estimation performance for Hurst parameters is shown to be satisfactory over finite samples.
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Dates et versions

hal-02361748 , version 1 (13-11-2019)

Identifiants

  • HAL Id : hal-02361748 , version 1

Citer

B Cooper Boniece, Gustavo Didier, Herwig Wendt, Patrice Abry. ON MULTIVARIATE NON-GAUSSIAN SCALE INVARIANCE: FRACTIONAL LÉVY PROCESSES AND WAVELET ESTIMATION. European Signal Processing Conference (EUSIPCO), Sep 2019, A Coruna, Spain. ⟨hal-02361748⟩
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