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Conference proceeding
On Multivariate Non-Gaussian Scale Invariance: Fractional Lévy Processes And Wavelet Estimation
2019 27th European Signal Processing Conference (EUSIPCO), v 2019-, pp 1-5
Sep 2019
Abstract
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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Details
- Title
- On Multivariate Non-Gaussian Scale Invariance: Fractional Lévy Processes And Wavelet Estimation
- Creators
- B. Cooper Boniece - Tulane UniversityGustavo Didier - Tulane UniversityHerwig Wendt - Institut Polytechnique de BordeauxPatrice Abry - École Normale Supérieure de Lyon
- Publication Details
- 2019 27th European Signal Processing Conference (EUSIPCO), v 2019-, pp 1-5
- Publisher
- EURASIP
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000604567700254
- Scopus ID
- 2-s2.0-85075606800
- Other Identifier
- 991021861641904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic