Displaying similar documents to “Extremal and additive processes generated by Pareto distributed random vectors”

Linear distribution processes.

Bel, L., Oppenheim, G., Robbiano, L., Viano, M.C. (1998)

Journal of Applied Mathematics and Stochastic Analysis

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On the large deviations of a class of modulated additive processes

Ken R. Duffy, Claudio Macci, Giovanni Luca Torrisi (2011)

ESAIM: Probability and Statistics

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We prove that the large deviation principle holds for a class of processes inspired by semi-Markov additive processes. For the processes we consider, the sojourn times in the phase process need not be independent and identically distributed. Moreover the state selection process need not be independent of the sojourn times. We assume that the phase process takes values in a finite set and that the order in which elements in the set, called states, are visited is selected stochastically....

On the large deviations of a class of modulated additive processes

Ken R. Duffy, Claudio Macci, Giovanni Luca Torrisi (2012)

ESAIM: Probability and Statistics

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We prove that the large deviation principle holds for a class of processes inspired by semi-Markov additive processes. For the processes we consider, the sojourn times in the phase process need not be independent and identically distributed. Moreover the state selection process need not be independent of the sojourn times. We assume that the phase process takes values in a finite set and that the order in which elements in the set, called states, are visited is selected stochastically....

Distributions that are functions

Ricardo Estrada (2010)

Banach Center Publications

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It is well-known that any locally Lebesgue integrable function generates a unique distribution, a so-called regular distribution. It is also well-known that many non-integrable functions can be regularized to give distributions, but in general not in a unique fashion. What is not so well-known is that to many distributions one can associate an ordinary function, the function that assigns the distributional point value of the distribution at each point where the value exists, and that...