DocumentCode :
3029154
Title :
ARD: An automated replication-deletion method for simulation analysis
Author :
Lada, Emily K. ; Mokashi, Anup C. ; Wilson, James R.
Author_Institution :
SAS Inst. Inc., Cary, NC, USA
fYear :
2013
fDate :
8-11 Dec. 2013
Firstpage :
802
Lastpage :
813
Abstract :
ARD is an automated replication-deletion procedure for computing point and confidence interval (CI) estimators for the steady-state mean of a simulation-generated output process. The CI can have user-specified values for its absolute or relative precision and its coverage probability. To compensate for skewness in the truncated sample mean for each replication, the CI incorporates a skewness adjustment. With increasingly stringent precision requirements, ARD´s sampling plan increases the run length and number of runs so as to minimize a weighted average of the mean squared errors of the following: (i) the grand mean of the truncated sample means for all runs; and (ii) the conventional replication-deletion estimator of the standard error of (i). We explain the operation of ARD, and we summarize an experimental performance evaluation of ARD. Although ARD´s CIs closely conformed to given coverage and precision requirements, ARD generally required a larger computing budget than single-run procedures.
Keywords :
estimation theory; mean square error methods; minimisation; probability; simulation; ARD; CI estimator; absolute precision; automated replication-deletion method; automated replication-deletion procedure; confidence interval estimator; conventional replication-deletion estimator; coverage probability; experimental performance evaluation; mean squared errors; point estimator; relative precision; sampling plan; simulation analysis; simulation-generated output process; skewness adjustment; skewness compensation; standard error; steady-state mean; truncated sample means; weighted average minimization; Algorithm design and analysis; Analytical models; Computational modeling; Data models; Standards; Steady-state;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference (WSC), 2013 Winter
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4799-2077-8
Type :
conf
DOI :
10.1109/WSC.2013.6721472
Filename :
6721472
Link To Document :
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