Abstract
Statistical ranking and selection (R&S) is a collection of experiment design and analysis techniques for selecting the "population" with the largest or smallest mean performance from among a finite set of alternatives. R&S procedures have received considerable research attention in the stochastic simulation community, and they have been incorporated in commercial simulation software. One of the ways that R&S procedures are evaluated and compared is via the expected number of samples (often replications) that must be generated to reach a decision. In this paper we argue that sampling cost alone does not adequately characterize the efficiency of ranking-and-selection procedures, and we introduce a new sequential procedure that provides the same statistical guarantees as existing procedures while reducing the expected total cost of application.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 474-480 |
| Number of pages | 7 |
| Journal | Winter Simulation Conference Proceedings |
| Volume | 1 |
| State | Published - 2003 |
| Externally published | Yes |
| Event | Proceedings of the 2003 Winter Simulation Conference: Driving Innovation - New Orleans, LA, United States Duration: Dec 7 2003 → Dec 10 2003 |