Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3265
Title: Multiply reflected variance estimators for simulation
Authors: Dingeç, Kemal Dinçer
Alexopoulos, Christos
Goldsman, David
Meterelliyoz Kuyzu, Melike
Wilson, James R.
36211
Keywords: Computer Science, Theory & Methods
Issue Date: 2018
Publisher: IEEE
Source: Dingeç, K. D., Alexopoulos, C., Goldsman, D., Meterelliyoz, M., & Wilson, J. R. (2018, December). Multiply reflected variance estimators for simulation. In 2018 Winter Simulation Conference (WSC) (pp. 1670-1681). IEEE.
Series/Report no.: 2018 WINTER SIMULATION CONFERENCE (WSC)
Abstract: In a previous article, we studied a then-new class of standardized time series (STS) estimators for the asymptotic variance parameter of a stationary simulation output process. Those estimators invoke the well-known reflection principle of Brownian motion on the suitably standardized original output process to compute several "reflected" realizations of the STS, each of which is based on a single reflection point. We then calculated variance-and mean-squared-error-optimal linear combinations of the estimators formed from the singly reflected realizations. The current paper repeats the exercise except that we now examine the efficacy of employing multiple reflection points on each reflected realization of the STS. This scheme provides additional flexibility that can be exploited to produce estimators that are superior to their single-reflection-point predecessors with respect to mean-squared error. We illustrate the enhanced performance of the multiply reflected estimators via exact calculations and Monte Carlo experiments.
URI: https://ieeexplore.ieee.org/abstract/document/8632554
10.1109/WSC.2018.8632554
https://hdl.handle.net/20.500.11851/3265
ISSN: 0891-7736
Appears in Collections:İşletme Bölümü / Department of Management
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

110
checked on Dec 26, 2022

Google ScholarTM

Check


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.