Variance and derivative estimation for virtual performance in simulation analytics

Yujing Lin, Barry L. Nelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Virtual performance is a class of time-dependent performance measures conditional on a particular event occurring at timet0 for a (possibly) nonstationary stochastic process; virtual waiting time of a customer arriving to a queue at time t0 is one example. Virtual statistics are estimators of the virtual performance. In this paper, we go beyond the mean to propose estimators for the variance, and for the derivative of the mean with respect to time, of virtual performance, examining both their small-sample and asymptotic properties. We also provide a modified K-fold cross validation method for tuning the parameter k for the difference-based variance estimator, and evaluate the performance of both variance and derivative estimators via controlled studies. The variance and derivative provide useful information that is not apparent in the mean of virtual performance.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1856-1867
Number of pages12
ISBN (Electronic)9781538634288
DOIs
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas
Period12/3/1712/6/17

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Variance and derivative estimation for virtual performance in simulation analytics'. Together they form a unique fingerprint.

Cite this