Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Simulation: a statistical perspective
Simulation: a statistical perspective
Simulation and optimization in production planning: a case study
Decision Support Systems
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Validation of Trace-Driven Simulation Models: Bootstrap Tests
Management Science
Resampling Methods: A Practical Guide to Data Analysis
Resampling Methods: A Practical Guide to Data Analysis
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Tests for regression models with heteroskedasticity of unknown form
Computational Statistics & Data Analysis
Regression models and experimental designs: a tutorial for simulation analysts
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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Classic linear regression metamodels and their concomitant experimental designs assume a univariate (not multivariate) simulation response and white noise. By definition, white noise is normally (Gaussian), independently (implying no common random numbers), and identically (constant variance) distributed with zero mean (valid metamodel). This advanced tutorial tries to answer the following questions: (i) How realistic are these classic assumptions in simulation practice? (ii) How can these assumptions be tested? (iii) If assumptions are violated, can the simulation's I/O data be transformed such that the analysis becomes correct? (iv) If such transformations cannot be applied, which alternative statistical methods (for example, generalized least squares, bootstrapping, jackknifing) can then be applied?