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首页> 美国政府科技报告 >Methods for Factor Screening in Computer Simulation Experiments.
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Methods for Factor Screening in Computer Simulation Experiments.

机译：计算机模拟实验中因子筛选的方法。

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The use of a computer simulation model may be viewed as an experiment in which a set of k input variables are combined to produce at least one output or response variable. As in any experimental situation, the design of a computer simulation experiment is important. In general, not all k input variables or factors will be equally important in their effect on the response variable(s). It is very common to find that only a subset, say g < k, of the original k factors are important in explaining the response. We usually do not know the value of g, or which g factors are important. The problem of experimentation and analysis to discover the size and composition of the subset of active factors g is called the factor screening problem. It is important to accurately identify the set of active factors. Failure to identify an active factor can result in serious bias in the analysis and conclusions drawn from the model, if that factor is subsequently ignored. Conversely, experimentation with negligible factors is undesirable as it consumes the resources of experimentation needlessly. This report contains a survey of the available statistical methodology useful in factor screening. It also discusses the relative merits of each approach, and provides guidelines for the development of a factor screening strategy. Several examples are presented that demonstrate the construction of factor screening experiments, and the interpretation of the results of such experiments. (Author)

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