An infinite dimensional central limit theorem for correlated martingales
Ilie Grigorescu (2004)
Annales de l'I.H.P. Probabilités et statistiques
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Ilie Grigorescu (2004)
Annales de l'I.H.P. Probabilités et statistiques
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Peccati, Giovanni (2007)
Electronic Communications in Probability [electronic only]
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Yimin Xiao (2006)
Annales de la faculté des sciences de Toulouse Mathématiques
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In this survey, we first review various forms of local nondeterminism and sectorial local nondeterminism of Gaussian and stable random fields. Then we give sufficient conditions for Gaussian random fields with stationary increments to be strongly locally nondeterministic (SLND). Finally, we show some applications of SLND in studying sample path properties of -Gaussian random fields. The class of random fields to which the results are applicable includes fractional Brownian motion, the...
Boufoussi, Brahim, Dozzi, Marco E., Guerbaz, Raby (2008)
Electronic Journal of Probability [electronic only]
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Hambly, Ben M., Jones, Liza A. (2007)
Electronic Journal of Probability [electronic only]
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Matsui, Muneya, Shieh, Narn-Rueih (2009)
Electronic Journal of Probability [electronic only]
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Davar Khoshnevisan (1997)
Séminaire de probabilités de Strasbourg
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Laure Coutin, Nicolas Victoir (2009)
ESAIM: Probability and Statistics
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We propose some construction of enhanced Gaussian processes using Karhunen-Loeve expansion. We obtain a characterization and some criterion of existence and uniqueness. Using rough-path theory, we derive some Wong-Zakai Theorem.
Leonenko, N.N., Anh, V.V. (2001)
Journal of Applied Mathematics and Stochastic Analysis
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Serge Cohen, Renaud Marty (2008)
Annales de l'I.H.P. Probabilités et statistiques
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This paper is devoted to establish an invariance principle where the limit process is a multifractional gaussian process with a multifractional function which takes its values in (1/2, 1). Some properties, such as regularity and local self-similarity of this process are studied. Moreover the limit process is compared to the multifractional brownian motion.