Sliced Latin hypercube designs with both branching and nested factors

Chen, Hao; Yang, Jinyu; Lin, Dennis K.J.; Liu, Min Qian

doi:10.1016/j.spl.2018.11.007

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…Although the nested factors are quantitative in both this paper and [1], they can be qualitative sometimes. Chen et al [12] constructed SLHDs with both the branching and nested factors being qualitative. In addition, we can also construct the second and third parts of an IBLHD using the OA-based idea ( [18][19][20]).…”

Section: Discussionmentioning

confidence: 99%

“…A design is said to be orthogonal if the correlation between any two distinct columns is zero. An SL(n, q, s) is called a sliced orthogonal LHD (SOLHD, [7][8][9][10][11][12]), denoted by SOL(n, q, s), if both the whole design and its slices are orthogonal. From the construction method in Algorithm 2.1, if the original SLHD in Step 2 is an SOLHD, then the obtained IBLHD will inherit the orthogonality to some extent.…”

Section: Nearly Orthogonal Iblhdsmentioning

confidence: 99%

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Construction of Improved Branching Latin Hypercube Designs

2021

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In this paper, we propose a new method, called the level-collapsing method, to construct branching Latin hypercube designs (BLHDs). The obtained design has a sliced structure in the third part, that is, the part for the shared factors, which is desirable for the qualitative branching factors. The construction method is easy to implement, and (near) orthogonality can be achieved in the obtained BLHDs. A simulation example is provided to illustrate the effectiveness of the new designs.

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Section: Discussionmentioning

confidence: 99%

Section: Nearly Orthogonal Iblhdsmentioning

confidence: 99%

Construction of Improved Branching Latin Hypercube Designs

2021

Self Cite

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“…This section considers the construction of BOLHDs with q branching factors and t shared factors. As in Section 3.2, we refer to the construction method of two‐layer SOLHDs in Chen et al (2019) to give the construction of the part of design for the shared factors. See Algorithm 4 for details.…”

Section: Bolhds With Multiple Branching Factorsmentioning

confidence: 99%

“…The constraint t ≤ s in Step 1 of Algorithm 2 is necessary, because there is no OLHD( s , t ) with s < t . In fact, the design D 3 constructed in Algorithm 2 is a two‐layer SOLHD proposed by Chen et al (2019).…”

Section: Construction Of Bolhds With Two Branching Factorsmentioning

confidence: 99%

“…Goos and Jones (2019) discussed the modelling of data from experiments with branching and nested factors as well as the optimal design of such experiments. Chen et al (2019) considered the case where branching factors and nested factors are both qualitative and proposed two‐layer sliced Latin hypercube designs (SLHDs) to suit such situations. Chen et al (2021) proposed the level‐collapsing method to construct BLHDs having a sliced structure in the part for the shared factors.…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal designs with branching and nested factors

Qiao

Liu

Jianfeng

2022

Stat

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Computer experiments with branching and nested factors are a common class of computer experiments, but it is challenging to construct designs for this type of experiments. In this paper, we define a special type of design called branching orthogonal Latin hypercube design (BOLHD). Such a design has an appealing structure, that is, no matter at each level of a branching factor or the level-combination of branching factors, the corresponding design points of nested factors form an orthogonal Latin hypercube design (OLHD). This structure makes it a good choice for designing computer experiments with branching and nested factors. We propose several deterministic construction methods when branching factors have the same number of levels. Based on sliced Latin hypercube designs (SLHDs), the proposed methods are easy to operate. Some construction results are tabulated for practical use.

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Maxpro Designs for Experiments with Multiple Types of Branching and Nested Factors

Yang,

Zhou

2024

Entropy

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Contemporary experiments often involve special factors known as branching factors. The levels of such factors determine the presence of some certain factors, referred to as nested factors. The design criteria for investigating the goodness of such designs are rarely developed. Furthermore, the existing criteria for such designs pay less attention to the space-filling property of low-dimensional projections of the design. The efficiencies of designs yielded by such criteria can markedly decrease when only a few factors are significant. To address this issue, this paper proposes a novel space-filling criterion based on the maximum projection criterion to evaluate the performance of the designs with branching and nested factors. A framework to construct optimal designs under the proposed criterion is also provided. Compared with the existing works, the resulting designs have better space-filling properties in all possible low-dimensional projections. Moreover, our strategy imposes no constraints on run size, level, and type of any factor, demonstrating its broad applicability.

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Sliced Latin hypercube designs with both branching and nested factors

Cited by 3 publications

References 19 publications

Construction of Improved Branching Latin Hypercube Designs

Construction of Improved Branching Latin Hypercube Designs

Orthogonal designs with branching and nested factors

Maxpro Designs for Experiments with Multiple Types of Branching and Nested Factors

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