Ranking and selection techniques with overlapping variance estimators for simulations

被引：0

作者：

Healey, Christopher M. ^{[1
]}

Goldsman, David ^{[1
]}

Kim, Seong-Hee ^{[1
]}

机构：

[1] H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA 30332-0205, United States

来源：

Sequential Analysis | 2009年 / 28卷 / 04期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1080/07474940903238334

中图分类号：

O212 [数理统计];

学科分类号：

摘要：

Some ranking and selection (R&S) procedures for steady-state simulation require estimates of the asymptotic variance parameter of each system to guarantee a certain probability of correct selection. In this paper, we show that the performance of such R&S procedures depends highly on the quality of the variance estimates that are used. In fact, we study the performance of R&S procedures using three new variance estimators-overlapping area, overlapping Cramér-von Mises, and overlapping modified jackknifed Durbin-Watson estimators-that show better long-run performance than other estimators previously used in conjunction with R&S procedures for steady-state simulations. © Taylor & Francis Group, LLC.

引用

页码：459 / 474

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