NDLI: Getting more from the data in a multinomial selection problem

Content Provider	IEEE Xplore Digital Library
Author	Miller, J.O. Nelson, B.L. Reilly, C.H.
Copyright Year	1996
Abstract	We consider the problem of determining which of k simulated systems is most likely to be the best per former based on some objective performance measure. The standard experiment is to generate v in dependent vector observations (replications) across the k- systems. A classical multinomial selection pro cedure, BEM (Bechhofer, Elmaghraby, and Morse), prescribes a minimum number of replications so that the probability of correctly selecting the true best system meets or exceeds a prespecified probability. Assuming that larger is better, BEM selects as best the system having the largest value of the performance measure in more replications than any other. We propose using these same v replications across k systems to form vk pseudoreplications (no longer in dependent) that contain one observation from each system, and again select as best the system having the largest value of the performance measure in more pseudoreplications than any other. We expect that this new procedure, AVC (all vector comparisons), dominates BEM in the sense that AVC will never require more independent replications than DEM to meet a prespecified probability of correct selection. We present analytical and simulation results to show how AVC fares versus BEM for different underly ing distribution families, different numbers of populations and various values of v. We also present results for the closely related problem of estimating the probability that a specific system is the best.
Starting Page	287
Ending Page	294
File Size	605548
Page Count	8
File Format	PDF
ISBN	0780333837
DOI	10.1109/WSC.1996.873291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Winter Simulation Conf. Board of Dir.
Subject Keyword	Automatic voltage control Industrial engineering Engineering management Welding Systems engineering and theory Analytical models Command and control systems Context modeling Structural engineering Earthquakes
Content Type	Text
Resource Type	Article

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