A fixed-sample-size method for estimating steady-state quantiles Conference Paper


Authors: Lolos, A.; Alexopoulos, C.; Goldsman, D.; Dingeç, K. D.; Mokashi, A. C.; Wilson, J. R.
Title: A fixed-sample-size method for estimating steady-state quantiles
Conference Title: 2023 Winter Simulation Conference (WSC)
Abstract: We propose FQUEST, a fully automated fixed-sample-size procedure for computing confidence intervals (CIs) for steady-state quantiles. The user provides a (simulation-generated) dataset of arbitrary size and specifies the required quantile and nominal coverage probability of the anticipated CI. FQUEST incorporates the simulation analysis methods of batching, standardized time series (STS), and sectioning. Preliminary experimentation with the waiting-time process in a congested M/M/1 queueing system showed that FQUEST performed well by delivering CIs with estimated coverage probability close to the nominal level, even in unfavorable circumstances where the sample sizes were inadequate. In the latter cases and for very small samples for steady-state quantile estimation, the close conformance of the CI coverage probability typically came at the expense of loose CI precision. © 2023 IEEE.
Journal Title Winter Simulation Conference. Proceedings
Conference Dates: 2023 Dec 10-13
Conference Location: San Antonio, TX
ISBN: 0891-7736
Publisher: Institute of Electrical and Electronics Engineers Inc.  
Date Published: 2023-01-01
Start Page: 457
End Page: 468
Language: English
DOI: 10.1109/wsc60868.2023.10407872
PROVIDER: scopus
DOI/URL:
Notes: Conference paper -- Source: Scopus
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