Factorial Sampling Plans for Preliminary Computational Experiments
作者:
MaxD. Morris,
期刊:
Technometrics
(Taylor Available online 1991)
卷期:
Volume 33,
issue 2
页码: 161-174
ISSN:0040-1706
年代: 1991
DOI:10.1080/00401706.1991.10484804
出版商: Taylor & Francis Group
关键词: Computational model;Factor screening;Latin hypercube sampling;Sensitivity analysis
数据来源: Taylor
摘要:
Acomputational modelis a representation of some physical or other system of interest, first expressed mathematically and then implemented in the form of a computer program; it may be viewed as a function ofinputsthat, when evaluated, producesoutputs. Motivation for this article comes from computational models that are deterministic, complicated enough to make classical mathematical analysis impractical and that have a moderate-to-large number of inputs. The problem of designing computational experiments to determine which inputs have important effects on an output is considered. The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observedelementary effects, those changes in an output due solely to changes in a particular input. Advantages of this approach include a lack of reliance on assumptions of relative sparsity of important inputs, monotonicity of outputs with respect to inputs, or adequacy of a low-order polynomial as an approximation to the computational model.
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