Search results for "60H07"

showing 8 items of 8 documents

Malliavin smoothness on the L\'evy space with H\"older continuous or $BV$ functionals

2018

We consider Malliavin smoothness of random variables $f(X_1)$, where $X$ is a pure jump L\'evy process and $f$ is either bounded and H\"older continuous or of bounded variation. We show that Malliavin differentiability and fractional differentiability of $f(X_1)$ depend both on the regularity of $f$ and the Blumenthal-Getoor index of the L\'evy measure.

Mathematics::Probability60G51 60H07Mathematics - Probability
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Almost sure central limit theorems for random ratios and applications to lse for fractional ornstein–uhlenbeck processes

2012

We investigate an almost sure limit theorem (ASCLT) for sequences of random variables having the form of a ratio of two terms such that the numerator satisfies the ASCLT and the denominator is a positive term which converges almost surely to 1. This result leads to the ASCLT for least square estimators for Ornstein-Uhlenbeck process driven by fractional Brownian motion.

Mathematics::ProbabilityProbability (math.PR)FOS: MathematicsMathematics - Probability60F05 60G15 60H05 60H07
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Malliavin derivative of random functions and applications to L��vy driven BSDEs

2014

We consider measurable $F: ��\times \mathbb{R}^d \to \mathbb{R}$ where $F(\cdot, x)$ belongs for any $x$ to the Malliavin Sobolev space $\mathbb{D}_{1,2}$ (with respect to a L��vy process) and provide sufficient conditions on $F$ and $G_1,\ldots,G_d \in \mathbb{D}_{1,2}$ such that $F(\cdot, G_1,\ldots,G_d) \in \mathbb{D}_{1,2}.$ The above result is applied to show Malliavin differentiability of solutions to BSDEs (backward stochastic differential equations) driven by L��vy noise where the generator is given by a progressively measurable function $f(��,t,y,z).$

Probability (math.PR)FOS: Mathematics60H07 60G51 60H10
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Decoupling on the Wiener Space, Related Besov Spaces, and Applications to BSDEs

2021

We introduce a decoupling method on the Wiener space to define a wide class of anisotropic Besov spaces. The decoupling method is based on a general distributional approach and not restricted to the Wiener space. The class of Besov spaces we introduce contains the traditional isotropic Besov spaces obtained by the real interpolation method, but also new spaces that are designed to investigate backwards stochastic differential equations (BSDEs). As examples we discuss the Besov regularity (in the sense of our spaces) of forward diffusions and local times. It is shown that among our newly introduced Besov spaces there are spaces that characterize quantitative properties of directional derivat…

Pure mathematicsGeneral MathematicsType (model theory)Directional derivativeSpace (mathematics)Computer Science::Digital LibrariesStochastic differential equationQuadratic equationFOS: MathematicsAnisotropic Besov spacesMathematicsstokastiset prosessitosittaisdifferentiaaliyhtälöt60H07 60H10 46E35Applied MathematicsProbability (math.PR)Decoupling (cosmology)interpolationFunctional Analysis (math.FA)Mathematics - Functional Analysisbackward stochastic differential equationsComputer Science::Mathematical Softwaredecoupling on the Wiener spacefunktionaalianalyysiMathematics - ProbabilityGenerator (mathematics)Interpolation
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Ergodicity for a stochastic Hodgkin–Huxley model driven by Ornstein–Uhlenbeck type input

2013

We consider a model describing a neuron and the input it receives from its dendritic tree when this input is a random perturbation of a periodic deterministic signal, driven by an Ornstein-Uhlenbeck process. The neuron itself is modeled by a variant of the classical Hodgkin-Huxley model. Using the existence of an accessible point where the weak Hoermander condition holds and the fact that the coefficients of the system are analytic, we show that the system is non-degenerate. The existence of a Lyapunov function allows to deduce the existence of (at most a finite number of) extremal invariant measures for the process. As a consequence, the complexity of the system is drastically reduced in c…

Statistics and ProbabilityDegenerate diffusion processesWeak Hörmander conditionType (model theory)01 natural sciencesPeriodic ergodicity010104 statistics & probability60H0760J25FOS: Mathematics0101 mathematicsComputingMilieux_MISCELLANEOUSMathematical physicsMathematics60J60Quantitative Biology::Neurons and CognitionProbability (math.PR)010102 general mathematicsErgodicityOrnstein–Uhlenbeck processHodgkin–Huxley model[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Hodgkin–Huxley model60J60 60J25 60H07Statistics Probability and UncertaintyTime inhomogeneous diffusion processesMathematics - Probability
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Malliavin Calculus and Skorohod Integration for Quantum Stochastic Processes

2000

A derivation operator and a divergence operator are defined on the algebra of bounded operators on the symmetric Fock space over the complexification of a real Hilbert space $\eufrak{h}$ and it is shown that they satisfy similar properties as the derivation and divergence operator on the Wiener space over $\eufrak{h}$. The derivation operator is then used to give sufficient conditions for the existence of smooth Wigner densities for pairs of operators satisfying the canonical commutation relations. For $\eufrak{h}=L^2(\mathbb{R}_+)$, the divergence operator is shown to coincide with the Hudson-Parthasarathy quantum stochastic integral for adapted integrable processes and with the non-causal…

Statistics and ProbabilityPure mathematics[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Integrable systemComplexificationSpace (mathematics)Malliavin calculus01 natural sciences81S25Fock space81S25; 60H07; 60G15010104 statistics & probabilitysymbols.namesakeOperator (computer programming)60H07FOS: Mathematics0101 mathematicsMathematical PhysicsMathematicsApplied Mathematics010102 general mathematicsProbability (math.PR)Hilbert spaceStatistical and Nonlinear Physics[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Bounded function60G15symbols[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - Probability
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A note on Malliavin smoothness on the Lévy space

2017

We consider Malliavin calculus based on the Itô chaos decomposition of square integrable random variables on the Lévy space. We show that when a random variable satisfies a certain measurability condition, its differentiability and fractional differentiability can be determined by weighted Lebesgue spaces. The measurability condition is satisfied for all random variables if the underlying Lévy process is a compound Poisson process on a finite time interval. peerReviewed

Statistics and ProbabilitySmoothness (probability theory)matematiikkaLévy processMalliavin calculus010102 general mathematicsMalliavin calculus01 natural sciencesLévy processinterpolation010104 statistics & probability60H07Mathematics::ProbabilitySquare-integrable functionCompound Poisson processApplied mathematicsinterpolointiDifferentiable functiontila0101 mathematicsStatistics Probability and UncertaintyLp spaceRandom variable60G51MathematicsElectronic Communications in Probability
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Simulation of BSDEs with jumps by Wiener Chaos Expansion

2016

International audience; We present an algorithm to solve BSDEs with jumps based on Wiener Chaos Expansion and Picard's iterations. This paper extends the results given in Briand-Labart (2014) to the case of BSDEs with jumps. We get a forward scheme where the conditional expectations are easily computed thanks to chaos decomposition formulas. Concerning the error, we derive explicit bounds with respect to the number of chaos, the discretization time step and the number of Monte Carlo simulations. We also present numerical experiments. We obtain very encouraging results in terms of speed and accuracy.

Statistics and ProbabilityWiener Chaos expansionDiscretizationMonte Carlo methodTime stepConditional expectation01 natural sciences010104 statistics & probabilitybackward stochastic differential equations with jumpsFOS: MathematicsApplied mathematics60H10 60J75 60H35 65C05 65G99 60H070101 mathematicsMathematicsPolynomial chaosApplied MathematicsNumerical analysis010102 general mathematicsMathematical analysista111Probability (math.PR)numerical methodCHAOS (operating system)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Modeling and SimulationScheme (mathematics)Mathematics - Probability
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