Simulating Stable Stochastic Systems, VI: Quantile Estimation

Author:

Iglehart Donald L.1

Affiliation:

1. Department of Operations Research, Stanford University, Stanford, CA

Abstract

In this paper the author continues his study of the regenerative method for analyzing simulations of stable stochastic systems. The principal concern is to estimate the quantiles of the stationary distribution of a regenerative process. Markov chains in discrete or continuous time and multiple server queues in light traffic provide concrete examples of regenerative processes to which this technique applies. Approximate confidence intervals for these quantiles are derived from appropriate central limit theorems. The method has been applied to three stochastic simulations, and the numerical results are presented.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Hardware and Architecture,Information Systems,Control and Systems Engineering,Software

Reference11 articles.

1. Simulating Stable Stochastic Systems, I: General Multiserver Queues

2. Simulating Stable Stochastic Systems, II: Markov Chains

3. Simulating stable stochastzc systems, III: Regenerative processes and discrete-event simulations;CRANE M.A.;Oper. Res,1975

4. Simulating stable stochastic systems, IV: Approximation techniques;CRANE M.A;Manage. Sc~.,1975

5. GOODMAN A.S LEwis P.A.W. A~D ROBmNS H.E. Simultaneous estimation of a large number of extreme quantiles in simulation experiments. (Submitted to a techmca} journal ) GOODMAN A.S LEwis P.A.W. A~D ROBmNS H.E. Simultaneous estimation of a large number of extreme quantiles in simulation experiments. (Submitted to a techmca} journal )

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