A sequential method for estimating steady-state quantiles using standardized time series Conference Paper
| Authors: | Lolos, A.; Boone, J. H.; Alexopoulos, C.; Goldsman, D.; Dingec, K. D.; Mokashi, A. C.; Wilson, J. R. |
| Title: | A sequential method for estimating steady-state quantiles using standardized time series |
| Conference Title: | 2022 Winter Simulation Conference |
| Abstract: | We propose SQSTS, an automated sequential procedure for computing confidence intervals (CIs) for steady-state quantiles based on Standardized Time Series (STS) processes computed from sample quantiles. We estimate the variance parameter associated with a given quantile estimator using the order statistics of the full sample and a combination of variance-parameter estimators based on the theoretical framework developed by Alexopoulos et al. in 2022. SQSTS is structurally less complicated than its main competitors, the Sequest and Sequem methods developed by Alexopoulos et al. in 2019 and 2017. Preliminary experimentation with the customer delay process prior to service in a congested M/M/1 queueing system revealed that SQSTS performed favorably compared with Sequest and Sequem in terms of the estimated CI coverage probability, and it significantly outperformed the latter methods with regard to average sample-size requirements. © 2022 IEEE. |
| Keywords: | steady state; confidence interval; time series; order-statistics; parameters estimators; quantile estimators; sequential methods; sequential procedures; sequest; standardized time; times series |
| Journal Title | Proceedings of the 2022 Winter Simulation Conference |
| Conference Dates: | 2022 Dec 11-14 |
| Conference Location: | Singapore |
| ISBN: | 978-1-6654-7662-1 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Date Published: | 2022-01-01 |
| Start Page: | 73 |
| End Page: | 84 |
| Language: | English |
| DOI: | 10.1109/wsc57314.2022.10015283 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference Paper -- This meeting was also held virtually -- Export Date: 1 March 2023 -- Source: Scopus |
