Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Experiments in concurrent stochastic simulation: the EcliPSe paradigm
Journal of Parallel and Distributed Computing
Confidence intervals for univariate discrete-event simulation output using the Kalman filter
WSC '92 Proceedings of the 24th conference on Winter simulation
Distributed stochastic discrete-event simulation in parallel time streams
WSC '94 Proceedings of the 26th conference on Winter simulation
Cramér-von Mises variance estimators for simulations
WSC '91 Proceedings of the 23rd conference on Winter simulation
Large and small sample comparisons of various variance estimators
WSC '86 Proceedings of the 18th conference on Winter simulation
A conceptual framework for research in the analysis of simulation output
Communications of the ACM - Special issue on simulation modeling and statistical computing
A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Coverage error for confidence intervals arising in simulation output analysis
WSC '82 Proceedings of the 14th conference on Winter Simulation - Volume 2
Confidence intervals for queueing simulations of computer systems
ACM SIGMETRICS Performance Evaluation Review
| Hi-index | 0.00 |
Sequential analysis of simulation output is generally accepted as the most efficient way for securing representativeness of samples of collected observations. In this scenario a simulation experiment is stopped when the relative precision of estimates, defined as the relative width of confidence intervals at an assumed confidence level, reaches the required level. This paper deals with the statistical correctness of the methods proposed for estimating confidence intervals for mean values in sequential steady-state stochastic simulation. We formulate basic rules that should be followed in proper experimental analysis of coverage of different steady state interval estimators. Our main argument is that such analysis should be done sequentially. The numerical results of our preliminary coverage analysis of the method of Spectral Analysis (SA/HW) and Non overlapping Batch Means are presented, and compared with those obtained by traditional, non-sequential approaches.