Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Random number generators: good ones are hard to find
Communications of the ACM
Simulation: a statistical perspective
Simulation: a statistical perspective
Regression metamodels and design of experiments
WSC '96 Proceedings of the 28th conference on Winter simulation
Principles of simulation model validation, verification, and testing
Transactions of the Society for Computer Simulation International
Validation of Trace-Driven Simulation Models: a Novel Regression Test
Management Science
Simulation model verification and validation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Introduction to Simulation Using SIMAN
Introduction to Simulation Using SIMAN
Simulation Validation - a Confidence Assessment Methodology
Simulation Validation - a Confidence Assessment Methodology
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Proceedings of the 30th conference on Winter simulation
Validation of models: statistical techniques and data availability
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
A prescriptive technique for V&V of simulation models when no real-life data are available
Proceedings of the 38th conference on Winter simulation
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For the validation of trace-driven simulation models this paper recommends a simple statistical test that uses elementary regression analysis in a novel way. This test concerns a (joint) null-hypothesis: the outputs of the simulated and the real systems have the same means and the same variances. Technically, the differences between simulated and real outputs are regressed on their sums, and the resulting slope and intercept are tested to be zero. This paper further proves that it is wrong to use a naive test that regresses the simulation outputs on the real outcomes, and hypothesizes that the resulting regression line gives a 45 /spl deg/ line through the origin. The new and the old tests are investigated in Monte Carlo experiments with inventory systems. The conclusion is that the new test has the correct type I error probability, whereas the old test (falsely) rejects a valid simulation model substantially more often than the nominal alpha level. The power of the new test increases, as the simulation model deviates more from the real system.