Name
Abstract | ||
|---|---|---|
The finite-difference method has been commonly used in stochastic derivative estimation when an unbiased derivative estimator is unavailable or costly. The efficiency of this method relies on the choice of a perturbation parameter, which needs to be calibrated based on the number of simulation replications. We study the setting where such an a priori planning of simulation runs is difficult, which could arise due to the variability of runtime for complex simulation models or interruptions. We show how a simple recursive weighting scheme on simulation outputs can recover, in an online fashion, the optimal asymptotic bias-variance tradeoff achieved by the conventional scheme where the replication size is known in advance.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WSC.2018.8632325 | WSC |
Field | DocType | ISSN |
Mathematical optimization,Weighting,Computer science,Simulation,A priori and a posteriori,Stochastic process,Bias–variance tradeoff,Simulation modeling,Perturbation (astronomy),Recursion,Estimator | Conference | 0891-7736 |
ISBN | Citations | PageRank |
978-1-5386-6570 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thibault Duplay | 1 | 0 | 0.34 |
| Henry Lam | 2 | 74 | 18.01 |
| Xinyu Zhang | 3 | 24 | 12.48 |