The transient behavior of the M/Ek/2 queue and steady-state simulation
Computers and Operations Research
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
An investigation of finite-sample behavior of confidence interval estimators
Operations Research
Weighted batch means for confidence intervals in steady-state simulations
Management Science
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
An approach for finding discrete variable design alternatives using a simulation optimization method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
WSC '04 Proceedings of the 36th conference on Winter simulation
Hi-index | 0.00 |
This paper presents a new approach to estimating and constructing confidence intervals for the steady state mean of a stochastic process from short simulations which may exhibit significant transient response. Specifically, we examine the conditional least squares estimator for the mean of an autoregressive process. If the process is autoregressive with normal innovations, this estimator is the conditional maximum likelihood estimate (MLE) of the mean. We show that the MLE is asymptotically normal, and derive a finite-sample approximation to its distribution. This provides the basis for two asymptotically valid single-replication confidence intervals which do not require choosing a batch size. As a point estimator, the MLE is a generalization of the estimator due to Snell and Schruben (1984) and is related to the weighted batch mean (Bischak, Kelton, and Pollock, 1993). Empirical results for a queuing network show that the autoregressive process is a reasonable model of transient response. For short series with reasonable initializations (e.g., empty and idle), the MLE yields confidence inter vals which are comparable or superior to those of existing procedures, in both single and parallel replication simulations.