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Patrice Abry
Researcher at École normale supérieure de Lyon
Publications - 379
Citations - 10269
Patrice Abry is an academic researcher from École normale supérieure de Lyon. The author has contributed to research in topics: Wavelet & Multifractal system. The author has an hindex of 48, co-authored 360 publications receiving 9410 citations. Previous affiliations of Patrice Abry include École Normale Supérieure & Royal Meteorological Institute.
Papers
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Journal ArticleDOI
Wavelet analysis of long-range-dependent traffic
Patrice Abry,Darryl Veitch +1 more
TL;DR: A wavelet-based tool for the analysis of long-range dependence and a related semi-parametric estimator of the Hurst parameter is introduced and is shown to be unbiased under very general conditions, and efficient under Gaussian assumptions.
Journal ArticleDOI
A wavelet-based joint estimator of the parameters of long-range dependence
Darryl Veitch,Patrice Abry +1 more
TL;DR: A joint estimator is presented for the two parameters that define the long-range dependence phenomenon in the simplest case and is found to be unbiased and of minimum or close to minimum variance for the scale parameter, and asymptotically unbiased and efficient for the second parameter.
Book ChapterDOI
Wavelets for the Analysis, Estimation, and Synthesis of Scaling Data
TL;DR: Long-range dependence-Scaling phenomena-(Multi)fractal-Wavelet analysis Scaling analysis-Sc scaling parameters estimation-Robustness-Fractional Brownian motion synthesis-Fano factor-Aggregation procedure-Allan variance.
Proceedings ArticleDOI
MAWILab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking
TL;DR: The goal of the present article is to assist researchers in the evaluation of detectors by providing them with labeled anomaly traffic traces by proposing a reliable graph-based methodology that combines any anomaly detector outputs.
Journal ArticleDOI
Bootstrap for Empirical Multifractal Analysis
TL;DR: The goal of this article is to show how non-parametric bootstrap approaches circumvent limitations and yield procedures that exhibit satisfactory statistical performance and can hence be practically used on real-life data.