Pseudo-Poissonian processes with stochastic intensity and a class of processes generalizing the Ornstein–Uhlenbeck process
Autor: | O. V. Rusakov |
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Rok vydání: | 2017 |
Předmět: |
Independent and identically distributed random variables
Generalization General Mathematics 010102 general mathematics Mathematical analysis Random function Ornstein–Uhlenbeck process Physics::Data Analysis Statistics and Probability 01 natural sciences Stability (probability) Random sequence 010305 fluids & plasmas Autocovariance 0103 physical sciences Statistical physics 0101 mathematics Random variable Mathematics |
Zdroj: | Vestnik St. Petersburg University, Mathematics. 50:153-160 |
ISSN: | 1934-7855 1063-4541 |
DOI: | 10.3103/s106345411702011x |
Popis: | The definition of pseudo-Poissonian processes is given in the famous monograph of William Feller (1971, Vol. II, Chapter X). The contemporary development of the theory of information flows generates new interest in the detailed analysis of behavior and characteristics of pseudo-Poissonian processes. Formally, a pseudo-Poissonian process is a Poissonian subordination of the mathematical time of an independent random sequence (the time randomization of a random sequence). We consider a sequence consisting of independent identically distributed random variables with second moments. In this case, pseudo-Poissonian processes do not have independent increments, but it is possible to calculate the autocovariance function, and it turns out that it exponentially decreases. Appropriately normed sums of independent copies of such pseudo-Poissonian processes tend to the Ornstein–Uhlenbeck process. A generalization of driving Poissonian processes to the case where the intensity is random is considered and it is shown that, under this generalization, the autocovariance function of the corresponding pseudo-Poissonian process is the Laplace transform of the distribution of that random intensity. Stochastic choice principles for the distribution of the random intensity are shortly discussed and they are illustrated by two detailed examples. |
Databáze: | OpenAIRE |
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