2016
DOI: 10.5705/ss.202015.0029
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A general theory for orthogonal array based Latin hypercube sampling

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Cited by 13 publications
(13 citation statements)
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“…The use of orthogonal arrays (OA), in specific designs for CEs, is suggested in refs. [ 21,22 ] OA combine two desirable properties: Stratification on multivariate margins and univariate uniformity. For more accuracy in estimation, strong OA with filling properties are recommended for CEs.…”
Section: Statistical Methods In the Design And Analysis Of Computer Ementioning
confidence: 99%
“…The use of orthogonal arrays (OA), in specific designs for CEs, is suggested in refs. [ 21,22 ] OA combine two desirable properties: Stratification on multivariate margins and univariate uniformity. For more accuracy in estimation, strong OA with filling properties are recommended for CEs.…”
Section: Statistical Methods In the Design And Analysis Of Computer Ementioning
confidence: 99%
“…It should be noted that the very definition of the set X S facilitates design of experiments which was a problem for both It is implemented using (13) and the appropriate mappings from the unit interval [0,1] n onto X S . Let { z ( k ) }, k = 1, …, N B , where z ( k ) = [ z 1 ( k ) … z n ( k ) ] T , denote the set of uniformly distributed data points in [0,1] n (here, using LHS). The mapping is realized in two stages.…”
Section: Nested Kriging Modelingmentioning
confidence: 99%
“…If these permutations are randomly sampled, clusters of points or regions of void space may be observed. For this reason, we base our design on the orthogonal arrays, which constrain the samples to fill the space with respect to a regular grid at a coarser scale than the LHS bins (Owen, 1992;Tang, 1993;Ai et al, 2016;Bevilacqua et al, 2019b;Patra et al, 2020).…”
Section: A3 Non-uniform Latin Hypercube Samplingmentioning
confidence: 99%