Displaying similar documents to “ L 1 -norm of infinitely divisible random vectors and certain stochastic integrals.”

Large deviations for independent random variables – Application to Erdös-Renyi’s functional law of large numbers

Jamal Najim (2005)

ESAIM: Probability and Statistics

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A Large Deviation Principle (LDP) is proved for the family 1 n 1 n 𝐟 ( x i n ) · Z i n where the deterministic probability measure 1 n 1 n δ x i n converges weakly to a probability measure R and ( Z i n ) i are d -valued independent random variables whose distribution depends on x i n and satisfies the following exponential moments condition: sup i , n 𝔼 e α * | Z i n | < + forsome 0 < α * < + . In this context, the identification of the rate function is non-trivial due to the absence of equidistribution. We rely on fine convex analysis to address this issue. Among...

Dominant eigenvalue problem for positive integral operators and its solution by Monte Carlo method

Jan Kyncl (1998)

Applications of Mathematics

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In this paper, a method of numerical solution to the dominant eigenvalue problem for positive integral operators is presented. This method is based on results of the theory of positive operators developed by Krein and Rutman. The problem is solved by Monte Carlo method constructing random variables in such a way that differences between results obtained and the exact ones would be arbitrarily small. Some numerical results are shown.

Cyclic random motions in d -space with directions

Aimé Lachal (2006)

ESAIM: Probability and Statistics

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We study the probability distribution of the location of a particle performing a cyclic random motion in d . The particle can take possible directions with different velocities and the changes of direction occur at random times. The speed-vectors as well as the support of the distribution form a polyhedron (the first one having constant sides and the other expanding with time ). The distribution of the location of the particle is made up of two components: a singular component (corresponding...