ORTHOGONAL-MAXIMIN LATIN HYPERCUBE DESIGNS

By V. Roshan Joseph, Ying Hung. 2008

325 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2a2011c1152e38d41d6088903d4945383b489776

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Citations in the corpus

A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “…further improve the space-filling property of LHD, the so-called optimal LHD has been developed by combining with some space-filling strategies, e.g., the maximin criterion (van Dam et al. 2007, 2009) and the orthogonal arrays criterion (Joseph and Hung 2008; Lin et al. 2009; Loeppky et al. 2012).”
Least squares polynomial chaos expansion: A review of sampling strategies (2017, background): “…with equal probability from the set Several modiﬁed versions of LH sampling such as orthogonal [129, 130, 13] and column-wise orthogonal [116], sliced and optimal sliced [96], and orthogonal-maximin [64] have been proposed to improve the efﬁciency and accuracy of the LH sampling approach.”
Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence (2013, background): “The introduction of Latin hypercube sampling has been succeeded by further optimisations [33] [34] [35] [36] [37], which usually target a better space-filling character.”
Optimal maximin $L_{1}$-distance Latin hypercube designs based on good lattice point designs (2018, methodology): “Some researchers worked on the L2-distance and proposed algorithms such as simulated annealing [Morris and Mitchell (1995), Joseph and Hung (2008), Ba, Myers and Brenneman (2015)] and swarm optimization algorithms [Moon, Dean and Santner (2011), Chen et al. (2013)] to construct maximin distance…”
Selecting random Latin hypercube dimensions and designs through estimation of maximum absolute pairwise correlation (2012, background): “For such situations, Latin hypercube (LH) sampling (McKay, Beckman, and Conover 1979) has proven to be an invaluable technique.”