Automated Estimation of Extreme Steady-State Quantiles via the Maximum Transformation
Author:
Affiliation:
1. Georgia Institute of Technology, Atlanta, GA, USA
2. SAS Institute Inc., Cary, NC, USA
3. North Carolina State University, Raleigh, NC, USA
Abstract
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,Modelling and Simulation
Link
https://dl.acm.org/doi/pdf/10.1145/3122864
Reference35 articles.
1. A Unified Framework for Numerically Inverting Laplace Transforms
2. Sequest: A sequential procedure for estimating steady-state quantiles
3. Sequem: Estimating extreme steady-state quantiles via the maximum transformation
4. A new perspective on batched quantile estimation
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1. Overlapping Batch Confidence Regions on the Steady-State Quantile Vector;2022 Winter Simulation Conference (WSC);2022-12-11
2. A Sequential Method for Estimating Steady-State Quantiles Using Standardized Time Series;2022 Winter Simulation Conference (WSC);2022-12-11
3. Steady-State Quantile Estimation Using Standardized Time Series;2020 Winter Simulation Conference (WSC);2020-12-14
4. A Tutorial on Quantile Estimation via Monte Carlo;Springer Proceedings in Mathematics & Statistics;2020
5. Sequest: A Sequential Procedure for Estimating Quantiles in Steady-State Simulations;Operations Research;2019-06-28
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