Virtual Statistics in Simulation via k Nearest Neighbors

Yujing Lin (), Barry L. Nelson () and Linda Pei ()
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Yujing Lin: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119
Linda Pei: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119

INFORMS Journal on Computing, 2019, vol. 31, issue 3, 576–592

Abstract: “Virtual statistics,” as we define them, are estimators of performance measures that are conditional on the occurrence of an event; virtual waiting time of a customer arriving to a queue at time τ 0 is one example of virtual performance. In this paper, we describe a k -nearest-neighbor method for estimating virtual performance postsimulation from the retained sample paths, examining both its small-sample and asymptotic properties and providing two approaches for measuring the error of the k -nearest-neighbor estimator. We implement leave-one-replication-out cross-validation for tuning a single parameter k to use for any time (or times) of interest and evaluate the prediction performance of the k -nearest-neighbor estimator via controlled studies. As a by-product, this paper motivates a different way of thinking about how to process the output from dynamic, discrete-event simulation.

Keywords: Simulation, Statistical Analysis; Statistics; Queues: Nonstationary (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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