A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Probability, statistics, and queueing theory with computer science applications
Probability, statistics, and queueing theory with computer science applications
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Appendix: A primer on heavy-tailed distributions
Queueing Systems: Theory and Applications
Estimating expected completion times with probabilistic job routing
Proceedings of the 38th conference on Winter simulation
Using parallel replications for sequential estimation of multiple steady state quantiles
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Tools for dependent simulation input with copulas
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
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Confidence intervals for the median of estimators or other quantiles were proposed as a substitute for usual confidence intervals in terminating and steady-state simulation. This is adequate since for many estimators the median and the expectation are close together or coincide, particularly if the sample size is large. Grouping data into batches is useful for median confidence intervals. The novel confidence intervals are easy to obtain, the variance of the estimator is not used. They are well suited for correlated simulation output data, apply to functions of estimators, and in simulation they seem to be particularly accurate, namely they follow the confidence level better than other confidence intervals. This paper states their accuracy which is the difference between the nominal confidence level and the actual coverage. The accuracy is evaluated with analytical models and simulation. For the estimation of quantiles by order statistics, the new confidence intervals are exact.