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Modern Stochastics: Theory and Applications
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A class of fractional Ornstein–Uhlenbeck processes mixed with a Gamma distribution

A class of fractional Ornstein-Uhlenbeck processes mixed with a Gamma distribution
Authors: Bianchi, Luigi Amedeo; Bonaccorsi, Stefano; Tubaro, Luciano;

A class of fractional Ornstein–Uhlenbeck processes mixed with a Gamma distribution

Abstract

We consider a sequence of fractional Ornstein–Uhlenbeck processes, that are defined as solutions of a family of stochastic Volterra equations with a kernel given by the Riesz derivative kernel, and leading coefficients given by a sequence of independent Gamma random variables. We construct a new process by taking the empirical mean of this sequence. In our framework, the processes involved are not Markovian, hence the analysis of their asymptotic behaviour requires some ad hoc construction. In our main result, we prove the almost sure convergence in the space of trajectories of the empirical means to a given Gaussian process, which we characterize completely.

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Keywords

T57-57.97, Applied mathematics. Quantitative methods, Probability (math.PR), Fractional processes, including fractional Brownian motion, Gamma mixing, Fractional Ornstein–Uhlenbeck processes, empirical means, 60G22 (Primary) 60G17 (Secondary), fractional Ornstein-Uhlenbeck processes, Fractional Ornstein–Uhlenbeck processes, empirical means, Gamma mixing, stochastic Volterra equations, generalized Wright function, stochastic Volterra equations, QA1-939, generalized Wright function, FOS: Mathematics, 60G22, Sample path properties, Mathematics, Mathematics - Probability

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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