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Uniformity criterion for designs with both qualitative and quantitative factors.
TL;DR: In this article, a new uniformity criterion, qualitative-quantitative discrepancy (QQD), is proposed for assessing the uniformity of designs with both qualitative and quantitative factors.
Abstract: Experiments with both qualitative and quantitative factors occur frequently in practical applications. Many construction methods for this kind of designs, such as marginally coupled designs, were proposed to pursue some good space-filling structures. However, few criteria can be adapted to quantify the space-filling property of designs involving both qualitative and quantitative factors. As the uniformity is an important space-filling property of a design, in this paper, a new uniformity criterion, qualitative-quantitative discrepancy (QQD), is proposed for assessing the uniformity of designs with both types of factors. The closed form and lower bounds of the QQD are presented to calculate the exact QQD values of designs and recognize the uniform designs directly. In addition, a connection between the QQD and the balance pattern is derived, which not only helps to obtain a new lower bound but also provides a statistical justification of the QQD. Several examples show that the proposed criterion is reasonable and useful since it can distinguish distinct designs very well.
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TL;DR: In this article, the authors developed the notions of minimax and maximin distance sets (designs) intended for use in the selection-of-sites problem when the underlying surface is modeled by a prior distribution and observations are made without error.
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TL;DR: In this article, the authors give linkages among uniformity measured by the discrete discrepancy, generalized minimum aberration, minimum moment aberration and uniformity measure by the centered L2-discrepancy/the wrap-around L2 discrepancy.
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36 citations