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# Documents  60G22 | enregistrements trouvés : 5

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## Fractional Poisson process: long-range dependence and applications in ruin theory Biard, Romain | CIRM H

Multi angle

Research talks;Probability and Statistics

We study a renewal risk model in which the surplus process of the insurance company is modeled by a compound fractional Poisson process. We establish the long-range dependence property of this non-stationary process. Some results for the ruin probabilities are presented in various assumptions on the distribution of the claim sizes.

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## Large scale reduction simple Clausel, Marianne | CIRM H

Multi angle

Research talks;Probability and Statistics

Consider a non-linear function $G(X_t)$ where $X_t$ is a stationary Gaussian sequence with long-range dependence. The usual reduction principle states that the partial sums of $G(X_t)$ behave asymptotically like the partial sums of the first term in the expansion of $G$ in Hermite polynomials. In the context of the wavelet estimation of the long-range dependence parameter, one replaces the partial sums of $G(X_t)$ by the wavelet scalogram, namely the partial sum of squares of the wavelet coefficients. Is there a reduction principle in the wavelet setting, namely is the asymptotic behavior of the scalogram for $G(X_t)$ the same as that for the first term in the expansion of $G$ in Hermite polynomial? The answer is negative in general. This paper provides a minimal growth condition on the scales of the wavelet coefficients which ensures that the reduction principle also holds for the scalogram. The results are applied to testing the hypothesis that the long-range dependence parameter takes a specific value. Joint work with François Roueff and Murad S. Taqqu

Keywords: long-range dependence; long memory; self-similarity; wavelet transform; estimation; hypothesis
testing
Consider a non-linear function $G(X_t)$ where $X_t$ is a stationary Gaussian sequence with long-range dependence. The usual reduction principle states that the partial sums of $G(X_t)$ behave asymptotically like the partial sums of the first term in the expansion of $G$ in Hermite polynomials. In the context of the wavelet estimation of the long-range dependence parameter, one replaces the partial sums of $G(X_t)$ by the wavelet scalogram, ...

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## Wavelets and stochastic processes: how the Gaussian world became sparse Unser, Michael | CIRM H

Multi angle

Special events;30 Years of Wavelets;Analysis and its Applications

We start with a brief historical account of wavelets and of the way they shattered some of the preconceptions of the 20th century theory of statistical signal processing that is founded on the Gaussian hypothesis. The advent of wavelets led to the emergence of the concept of sparsity and resulted in important advances in image processing, compression, and the resolution of ill-posed inverse problems, including compressed sensing. In support of this change in paradigm, we introduce an extended class of stochastic processes specified by a generic (non-Gaussian) innovation model or, equivalently, as solutions of linear stochastic differential equations driven by white Lévy noise. Starting from first principles, we prove that the solutions of such equations are either Gaussian or sparse, at the exclusion of any other behavior. Moreover, we show that these processes admit a representation in a matched wavelet basis that is "sparse" and (approximately) decoupled. The proposed model lends itself well to an analytic treatment. It also has a strong predictive power in that it justifies the type of sparsity-promoting reconstruction methods that are currently being deployed in the field.

Keywords: wavelets - fractals - stochastic processes - sparsity - independent component analysis - differential operators - iterative thresholding - infinitely divisible laws - Lévy processes
We start with a brief historical account of wavelets and of the way they shattered some of the preconceptions of the 20th century theory of statistical signal processing that is founded on the Gaussian hypothesis. The advent of wavelets led to the emergence of the concept of sparsity and resulted in important advances in image processing, compression, and the resolution of ill-posed inverse problems, including compressed sensing. In support of ...

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## An introduction to continuous-time stochastic processes:theory, models, and applications to finance, biology, and medicine Capasso, Vincenzo ; Bakstein, David | Birkhäuser 2015

Ouvrage

- xvi; 482 p.
ISBN 978-1-4939-2756-2

Modeling and simulation in science, engineering and technology

Localisation : Ouvrage RdC (CAPA)

processus stochastique # martingale # calcul de Ito # équation différentielle stochastique # processus gaussien # processus de Markov # processus de Lévy # bruit blanc # finance # assurance # biologie # médecine

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## Fractional fields and applications Cohen, Serge ; Istas, Jacques | Springer;Société de Mathématiques Appliquées et Industrielles 2013

Ouvrage

- xii; 270 p.
ISBN 978-3-642-36738-0

Mathématiques & applications , 0073

Localisation : Collection 1er étage

mouvement brownien # analyse fractale # champ stochastique # autosimilarité

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Ressources Electroniques (Depuis le CIRM)

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