TY - GEN
T1 - Revisiting direct bootstrap resampling for input model uncertainty
AU - Barton, Russell R.
AU - Lam, Henry
AU - Song, Eunhye
N1 - Funding Information:
Computations for this research were performed on the Penn State University’s Institute for CyberScience Advanced CyberInfrastructure (ICS-ACI). We gratefully acknowledge support from the National Science Foundation under grants CMMI-1542020 and CAREER CMMI-1653339/1834710.
Funding Information:
Computations for this research were performed on the Penn State University's Institute for CyberScience Advanced CyberInfrastructure (ICS-ACI). We gratefully acknowledge support from the National Science Foundation under grants CMMI-1542020 and CAREER CMMI-1653339/1834710.
PY - 2019/1/31
Y1 - 2019/1/31
N2 - Metamodel-based bootstrap methods for characterizing input model uncertainty have disadvantages for settings where there are a large number of input distributions, or when using empirical distributions to drive the simulation. Early direct bootstrapping of empirical distributions did not take into account the distinction between intrinsic and extrinsic variations in the resampled quantities. When the intrinsic uncertainty is large, the result is overcoverage of the bootstrap percentile intervals. We explore ways of accounting for both sources in direct bootstrap characterization of input model uncertainty, and study the impact on confidence interval (CI) coverage. Four new bootstrap-based CIs for the expected simulation output under the unknown true distribution are proposed, basic shrinkage CI, percentile shrinkage CI, basic hierarchical bootstrap CI, and percentile hierarchical bootstrap CI, and their empirical performances are demonstrated using an example.
AB - Metamodel-based bootstrap methods for characterizing input model uncertainty have disadvantages for settings where there are a large number of input distributions, or when using empirical distributions to drive the simulation. Early direct bootstrapping of empirical distributions did not take into account the distinction between intrinsic and extrinsic variations in the resampled quantities. When the intrinsic uncertainty is large, the result is overcoverage of the bootstrap percentile intervals. We explore ways of accounting for both sources in direct bootstrap characterization of input model uncertainty, and study the impact on confidence interval (CI) coverage. Four new bootstrap-based CIs for the expected simulation output under the unknown true distribution are proposed, basic shrinkage CI, percentile shrinkage CI, basic hierarchical bootstrap CI, and percentile hierarchical bootstrap CI, and their empirical performances are demonstrated using an example.
UR - http://www.scopus.com/inward/record.url?scp=85062616034&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062616034&partnerID=8YFLogxK
U2 - 10.1109/WSC.2018.8632335
DO - 10.1109/WSC.2018.8632335
M3 - Conference contribution
AN - SCOPUS:85062616034
T3 - Proceedings - Winter Simulation Conference
SP - 1635
EP - 1645
BT - WSC 2018 - 2018 Winter Simulation Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Winter Simulation Conference, WSC 2018
Y2 - 9 December 2018 through 12 December 2018
ER -