Goodness-of-fit techniques
Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Uniform and bootstrap resampling of empirical distributions
WSC '93 Proceedings of the 25th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Sensitivity of output performance measures to input distributions in queueing simulation modeling
Proceedings of the 29th conference on Winter simulation
Bayesian model selection when the number of components is unknown
Proceedings of the 30th conference on Winter simulation
Interactive implementation of optimal simulation experiment designs
Proceedings of the 30th conference on Winter simulation
Advanced input modeling for simulation experimentation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Analysis of simulation experiments by bootstrap resampling
Proceedings of the 33nd conference on Winter simulation
Simulation of manufacturing systems
Applied system simulation
Simulation input modeling: prior and candidate models in the Bayesian analysis of finite mixtures
Proceedings of the 35th conference on Winter simulation: driving innovation
Input modeling using quantile statistical methods
WSC '04 Proceedings of the 36th conference on Winter simulation
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Bootstrap methods are a natural adjunct of computer simulation experiments; both use resampling techniques to construct the statistical distributions of quantities of interest. In this paper we consider how bootstrap methods can be used in selecting appropriate input models for use in a computer simulation experiment. The proposed method uses a goodness of-fit statistic to decide on which of several competing input models should be used. We use bootstrapping to find the distribution of the test statistic under different assumptions as to which model is the correct fit. This allows the quality of fit of the different models to be compared. The bootstrapping process can be extended to the simulation experiment itself, allowing the effect of variability of estimated parameters on the simulation output to be assessed. The methodology is described and illustrated by application to a queueing example investigating the delays experienced by motorists caused by toll booths at a bridge river crossing.