Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Simulation: a statistical perspective
Simulation: a statistical perspective
Latin hypercube sampling as a tool in uncertainty analysis of computer models
WSC '92 Proceedings of the 24th conference on Winter simulation
Sensitivity analysis of model output: performance of the iterated fractional factorial design method
Computational Statistics & Data Analysis
Sensitivity analysis and optimization in simulation: design of experiments and case studies
WSC '95 Proceedings of the 27th conference on Winter simulation
Validation of Trace-Driven Simulation Models: a Novel Regression Test
Management Science
Theory of Modelling and Simulation
Theory of Modelling and Simulation
Risk analysis and sensitivity analysis: antithesis or synthesis?
ACM SIGSIM Simulation Digest
Regression metamodels and design of experiments
WSC '96 Proceedings of the 28th conference on Winter simulation
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
A simple model for assessing output uncertainty in stochastic simulation systems
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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This paper recommends the following sequence for the evaluation of simulation models. 1) Validation: the availability of data on the real system determines the proper type of statistical technique. 2) Screening: in the simulation's pilot phase the important inputs are identified through a novel technique, namely sequential bifurcation, which uses aggregation and sequential experimentation. 3) Sensitivity or what-if analysis: the important inputs are analyzed in more detail, including interactions between inputs; relevant techniques are design of experiments (DOE) and regression analysis. 4) Uncertainty or risk analysis: important environmental inputs may have values not precisely known, so the resulting uncertainties in the model outputs are quantified; techniques are Monte Carlo and Latin hypercube sampling. 5) Optimization: policy variables may be controlled, applying Response Surface Methodology (RSM), which combines DOE, regression analysis, and steepest-ascent hill-climbing. This paper summarizes case studies for each stage.