Analyzing transformation-based simulation metamodels

Irizarry, Maria De Los A.; Kühl, Michael; Lada, Emily K.

doi:10.1109/wsc.2000.899854

Cited by 1 publication

(1 citation statement)

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“…This may require a specialized analysis approach. For example, Irizarry et al (2003) develop the so-called MLE-Delta method after finding that constructing metamodels and backtransforming the results may yield highly biased point and confidence interval estimates of the original (untransformed) responses. Often, simpler methods that do not rely on transformation suffice, particularly when we seek insights rather then predictions.…”

Section: Checking the Assumptionsmentioning

confidence: 99%

State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments

Kleijnen

Sanchez

Lucas

et al. 2005

INFORMS Journal on Computing

386

212

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Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expertise who want to select a design and an appropriate analysis for their experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments-as opposed to real-world experiments-that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system, (2) finding robust decisions or policies as opposed to so-called optimal solutions, and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as having many more factors than in real-world experiments, and the sequential nature of the data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs, such as the ease of design construction, the flexibility for analysis, and efficiency considerations. Moreover, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, complicated metamodels require more simulation runs. We present several procedures to validate the metamodel estimated from a specific design, and we summarize a case study illustrating several of our major themes. We conclude with a discussion of areas that merit more work to achieve the potential benefits-either via new research or incorporation into standard simulation or statistical packages.

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Section: Checking the Assumptionsmentioning

confidence: 99%