Ch. 7. A review of design and modeling in computer experiments

Victoria C.P. Chen, Kwok Leung Tsui, Russell R. Barton, Janet K. Allen

Research output: Chapter in Book/Report/Conference proceedingChapter

51 Scopus citations

Abstract

In this chapter, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is primarily on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review separates the two primary tasks for metamodeling: (1) select an experimental design; (2) fit a statistical model. We provide an overview of the general strategy and discuss applications in electrical engineering, chemical engineering, mechanical engineering, and dynamic programming. Then, we dedicate a section to statistical modeling methods followed by a section on experimental designs. Designs are discussed in two paradigms, model-dependent and model-independent, to emphasize their different objectives. Both classical and modern methods are discussed.

Original languageEnglish (US)
Title of host publicationStatistics in Industry
EditorsR. Khattree, C.R. Rao
Pages231-261
Number of pages31
DOIs
StatePublished - 2003

Publication series

NameHandbook of Statistics
Volume22
ISSN (Print)0169-7161

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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