Stein's method, Gaussian processes and Palm measures, with applications to queueing

Fuente: arXiv
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Main Authors: Barbour, A. D., Ross, Nathan, Zheng, Guangqu
Format: Preprint
Published: 2021
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author Barbour, A. D.
Ross, Nathan
Zheng, Guangqu
author_facet Barbour, A. D.
Ross, Nathan
Zheng, Guangqu
contents We develop a general approach to Stein's method for approximating a random process in the path space $D([0,T]\to R^d)$ by a real continuous Gaussian process. We then use the approach in the context of processes that have a representation as integrals with respect to anunderlying point process, deriving a general quantitative Gaussian approximation. The error bound is expressed in terms of couplings of the original process to processes generated from the reduced Palm measures associated with the point process. As applications, we study certain $\text{GI}/\text{GI}/\infty$ queues in the "heavy traffic" regime.
format Preprint
id arxiv_https___arxiv_org_abs_2110_10365
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Stein's method, Gaussian processes and Palm measures, with applications to queueing
Barbour, A. D.
Ross, Nathan
Zheng, Guangqu
Probability
Primary 60G15, 60G55, secondary 60K25, 60F25
We develop a general approach to Stein's method for approximating a random process in the path space $D([0,T]\to R^d)$ by a real continuous Gaussian process. We then use the approach in the context of processes that have a representation as integrals with respect to anunderlying point process, deriving a general quantitative Gaussian approximation. The error bound is expressed in terms of couplings of the original process to processes generated from the reduced Palm measures associated with the point process. As applications, we study certain $\text{GI}/\text{GI}/\infty$ queues in the "heavy traffic" regime.
title Stein's method, Gaussian processes and Palm measures, with applications to queueing
topic Probability
Primary 60G15, 60G55, secondary 60K25, 60F25
url https://arxiv.org/abs/2110.10365