Name
Abstract | ||
|---|---|---|
Some ranking and selection (R&S) procedures for steady-state simulation require an estimate of the asymptotic variance parameter of each system to guarantee a certain probability of correct selection. We show that the performance of such R&S procedures depends on the quality of the variance estimates that are used. In this paper, we study the performance of R&S procedures with two new variance estimators --- overlapping area and overlapping Cramér-von Mises estimators --- which show better long-run performance than other estimators previously used in R&S problems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1351542.1351645 | Winter Simulation Conference |
Keywords | Field | DocType |
asymptotic variance,probability,parameter estimation,steady state,simulation | Ranking,Steady state simulation,Estimation theory,Statistics,Delta method,Mathematics,Estimator | Conference |
ISBN | Citations | PageRank |
1-4244-1306-0 | 4 | 0.56 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher G. Healey | 1 | 861 | 65.46 |
| David Goldsman | 2 | 904 | 159.71 |
| Seong-Hee Kim | 3 | 527 | 49.75 |