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Design and analysis of computer experiments with quantitative and qualitative inputs: A selective review

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Abstract Computer experiment refers to the study of complex systems by using computer simulations to emulate the physical system. Design and analysis of computer experiments have attracted great attention in past decades. However, many computer experiments involve not only quantitative inputs, but also qualitative inputs, which make the design and analysis more challenging. The Latin hypercube design and its variants are widely used in computer experiments, but mainly for the quantitative inputs. Constructing desirable emulators for computer experiments with qualitative inputs also remains a challenging problem due to the discrete nature of qualitative inputs. In this review, we describe a set of statistical approaches for design and analysis of computer experiments with both quantitative and qualitative factors. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Algorithmic Development > Statistics
Projections of the Latin hypercube design in Table onto two factors
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Boxplots of the log(1 − NSE) associated with “EC,” “MC,” “UC,” and “AD” for the computer model in the real application over 100 randomly chosen prediction sets of 20 input settings. Abbreviation: EC, exchangeable correlation; MC, multiplicative correlation; NSE, Nash‐Sutcliffe efficiency; UC, unrestrictive correlation
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Scatter plots of x1 versus x2, where rows of D2 corresponding to levels 0,1,2 of zi are marked by ×, ∘, and +: (a) the levels of z1; (b) the levels of z2
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Algorithmic Development > Statistics
Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

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