Ranking and selection techniques with overlapping variance estimators

Authors:
Christopher Healey;David Goldsman;Seong-Hee Kim
Affiliations:
Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA
Venue:
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Year:
2007

Citing 7
Cited 2

Properties of standardized time series weighted area variance estimators

Management Science
Simulation output analysis using standardized time series

Mathematics of Operations Research
Selection procedures with standardized time series variance estimators

Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Ranking and Selection for Steady-State Simulation: Procedures and Perspectives

INFORMS Journal on Computing
On the Asymptotic Validity of Fully Sequential Selection Procedures for Steady-State Simulation

Operations Research
Overlapping Variance Estimators for Simulation

Operations Research
Efficient Computation of Overlapping Variance Estimators for Simulation

INFORMS Journal on Computing

Recent advances in ranking and selection

Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
ADAPT Selection procedures to process correlated and non-normal data with batch means

Winter Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

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.