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Improving Ranking and Selection Techniques with Overlapping Variance Estimators
by
David Goldsman
Georgia Tech University, USA
Coauthors: Christopher M. Healey and Seong-Hee Kim
We study certain ranking and selection procedures in the context of steady-state simulation. We show how these procedures can be implemented with overlapping variance estimators, and that the resulting procedures have better long-run performance characteristics than the analogous procedures using other estimators.
Date received: February 27, 2007
Copyright © 2007 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cauc-28.