Simulating Sensitivities of Conditional Value at Risk
Management Science
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Stochastic Kriging for Simulation Metamodeling
Operations Research
Nested Simulation in Portfolio Risk Measurement
Management Science
Better simulation metamodeling: the why, what, and how of stochastic kriging
Winter Simulation Conference
Efficient Risk Estimation via Nested Sequential Simulation
Management Science
The effects of common random numbers on stochastic kriging metamodels
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Replication splitting and variance for simulating discrete-parameter stochastic processes
Operations Research Letters
Stochastic kriging for conditional value-at-risk and its sensitivities
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
Stochastic kriging has been studied as an effective metamodeling technique for approximating response surfaces in the context of stochastic simulation. In a simulation experiment, an analyst typically needs to estimate relevant metamodel parameters and further do prediction; therefore, the impact of parameter estimation on the performance of the metamodel-based predictor has drawn some attention in the literature. However, how the standard stochastic kriging predictor is affected by the presence of bias in finite-sample estimates has not yet been fully investigated. In this article, we study the predictive performance and investigate optimal budget allocation rules subject to a fixed computational budget constraint. Furthermore, we extend the analysis to two-level or nested simulation, which has been recently documented in the risk management literature, with biased estimators.