 Asymmetric uniform designs based on mixture discrepancyA. Elsawah () and Hong Qin Journal of Applied Statistics, 2016, vol. 43, issue 12, 2280-2294 Abstract: Efficient experimental design is crucial in the study of scientific problems. The uniform design is one of the most widely used approaches. The discrepancies have played an important role in quasi-Monte Carlo methods and uniform design. Zhou et al. [17] proposed a new type of discrepancy, mixture discrepancy ( $ {\mathcal {MD}} $ MD), and showed that $ {\mathcal {MD}} $ MD may be a better uniformity measure than other discrepancies. In this paper, we discuss in depth the $ {\mathcal{MD}} $ MD as the uniformity measure for asymmetric mixed two and three levels U-type designs. New analytical expression based on row distance and new lower bound of the $ {\mathcal {MD}} $ MD are given for asymmetric levels designs. Using the new formulation and the new lower bound as the benchmark, we can implement a new version of the fast local search heuristic threshold accepting. By this search heuristic, we can obtain mixed two and three levels U-type designs with low discrepancy. Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:43:y:2016:i:12:p:2280-2294 Ordering information: This journal article can be ordered from DOI: 10.1080/02664763.2016.1140727 Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
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