Limit Theorems for Markov Random Sets

1973 ◽  
Vol 17 (3) ◽  
pp. 426-433 ◽  
Author(s):  
L. I. Piterbarg
Keyword(s):  
Limit Theorems ◽  
Random Sets ◽  
Markov Random

Limit theorems for convex hulls of random sets

1993 ◽  
Vol 25 (02) ◽  
pp. 395-414 ◽  
Author(s):  
Ilya S. Molchanov
Keyword(s):  
Limit Theorems ◽  
Random Sets ◽  
Convex Hulls ◽  
Stable Sets ◽  
Limiting Distributions ◽  
Closed Set ◽  
Closed Sets ◽  
Random Closed Sets ◽  
Random Closed Set ◽  
Simple Limit

Let , be i.i.d. random closed sets in . Limit theorems for their normalized convex hulls conv () are proved. The limiting distributions correspond to C-stable random sets. The random closed set A is called C-stable if, for any , the sets anA and conv ( coincide in distribution for certain positive an, compact Kn , and independent copies A 1, …, An of A. The distributions of C-stable sets are characterized via corresponding containment functionals.

scholarly journals Central limit theorems for fuzzy random sets

2005 ◽  
Vol 15 (3) ◽  
pp. 337-342 ◽  
Author(s):  
Joong-Sung Kwon ◽  
Yun-Kyong Kim ◽  
Sang-Yeol Joo ◽  
Gyeong-Suk Choi
Keyword(s):  
Central Limit ◽  
Limit Theorems ◽  
Central Limit Theorems ◽  
Random Sets ◽  
Fuzzy Random Sets ◽  
Fuzzy Random

scholarly journals Controlled Discrete-Time Semi-Markov Random Evolutions and Their Applications

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 158
Author(s):  
Anatoliy Swishchuk ◽  
Nikolaos Limnios
Keyword(s):  
Dynamical Systems ◽  
Discrete Time ◽  
Diffusion Approximation ◽  
Limit Theorems ◽  
Stochastic Systems ◽  
Renewal Processes ◽  
Additive Functionals ◽  
Markov Random ◽  
Markov Renewal Processes ◽  
Random Evolutions

In this paper, we introduced controlled discrete-time semi-Markov random evolutions. These processes are random evolutions of discrete-time semi-Markov processes where we consider a control. applied to the values of random evolution. The main results concern time-rescaled weak convergence limit theorems in a Banach space of the above stochastic systems as averaging and diffusion approximation. The applications are given to the controlled additive functionals, controlled geometric Markov renewal processes, and controlled dynamical systems. We provide dynamical principles for discrete-time dynamical systems such as controlled additive functionals and controlled geometric Markov renewal processes. We also produce dynamic programming equations (Hamilton–Jacobi–Bellman equations) for the limiting processes in diffusion approximation such as controlled additive functionals, controlled geometric Markov renewal processes and controlled dynamical systems. As an example, we consider the solution of portfolio optimization problem by Merton for the limiting controlled geometric Markov renewal processes in diffusion approximation scheme. The rates of convergence in the limit theorems are also presented.

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