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Accueil du site > Séminaires > Probabilités Statistiques et réseaux de neurones > Variations and Hurst index estimation for a Rosenblatt process using longer filters.

Vendredi 12 juin 2009 à 11h00

Variations and Hurst index estimation for a Rosenblatt process using longer filters.

Frederi Viens (Purdue University, USA)

Résumé : The Rosenblatt process is a self-similar non-Gaussian process which lives in the second Wiener chaos, and occurs as the limit of correlated random sequences in so-called "non-central limit theorems". It shares the same covariance as fractional Brownian motion. We study the asymptotic distribution of the quadratic variations of the Rosenblatt process based on long filters, including filters based on high-order finite-difference and wavelet-based schemes. We find exact formulas for the limiting distributions, which we then use to devise strongly consistent estimators of the self-similarity parameter H. Unlike the case of fractional Brownian motion, no matter how high the filter orders are, the estimators are never asymptotically normal, converging instead in the mean square to the observed value of the Rosenblatt process at time 1.

This is joint work with Alexandra Chronopoulou and Ciprian Tudor.

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