A central limit theorem for stochastic recursive sequences of topical operators
Probability
2007-10-30 v3 Optimization and Control
Abstract
Let be a stationary sequence of topical (i.e., isotone and additively homogeneous) operators. Let be defined by and . It can model a wide range of systems including train or queuing networks, job-shop, timed digital circuits or parallel processing systems. When has the memory loss property, satisfies a strong law of large numbers. We show that it also satisfies the CLT if fulfills the same mixing and integrability assumptions that ensure the CLT for a sum of real variables in the results by P. Billingsley and I. Ibragimov.
Keywords
Cite
@article{arxiv.math/0606668,
title = {A central limit theorem for stochastic recursive sequences of topical operators},
author = {Glenn Merlet},
journal= {arXiv preprint arXiv:math/0606668},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/105051607000000168 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)