Comparison of Prediction Capabilities of Partially Replicated Central Composite Designs in Cuboidal Region

Ukaegbu, Eugene C.; Chigbu, Polycarp E.

doi:10.1080/03610926.2012.745561

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…They pointed out that replicating the axial portions of the orthogonal central composite design by far reduces the prediction variance, thus improving the G-efficiency in the spherical region. Also in [8], the author(s) considered variations of partially replicated central composite designs in the hypercube using the G-and Ioptimality criteria as well as the fraction of design space plots. They concluded that the replicated-star options always give smaller G-and I-optimal values than the corresponding replicated-cube options with equal number of replications.…”

Section: Introductionmentioning

confidence: 99%

Optimal Prediction Variance Capabilities of Inscribed Central Composite Designs

Nwanya

Dozie

2020

AJPAS

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This study looks at the effects of replication on prediction variance performances of inscribe central composite design especially those without replication on the factorial and axial portion (ICCD1), inscribe central composite design with replicated axial portion (ICCD2) and inscribe central composite design whose factorial portion is replicated (ICCD3). The G-optimal, I-optimal and FDS plots were used to examine these designs. Inscribe central composite design without replicated factorial and axial portion (ICCD1) has a better maximum scaled prediction variance (SPV) at factors k = 2 to 4 while inscribe central composite design with replicated factorial portion (ICCD3) has a better maximum and average SPV at 5 and 6 factor levels. The fraction of design space (FDS) plots show that the inscribe central composite design is superior to ICCD3 and inscribe central composite design with replicated axial portion (ICCD2) from 0.0 to 0.5 of the design space while inscribe central composite design with replicated factorial portion (ICCD3) is superior to ICCD1 and ICCD2 from 0.6 to 1.0 of the design space for factors k = 2 to 4.

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Section: Introductionmentioning

confidence: 99%

Optimal Prediction Variance Capabilities of Inscribed Central Composite Designs

Nwanya

Dozie

2020

AJPAS

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“…In comparing the prediction capabilities of some response surface designs , the Variance Dispersion Graph (VDG) proposed by Giovannitti-Jensen and Myers (1989) has been used; see, for example, Borkowski(1995), Li, Liang, Borror, Anderson-Cook and Montgomery (2009). Ukaegbu and Chigbu (2015) used the VDG and Fraction of Design Space (FDS) to compare the prediction variances of partially replicated ccd in cuboidal region and conclude that the replicated star is economically feasible than the replicated cube. VDG does not provide enough information on the effect of design on the prediction variance, hence, Khuri, Kim and Um (1996) proposed a method for describing the distribution of the predicted variances on a given sphere in terms of its quantiles.…”

Section: Introductionmentioning

confidence: 99%

Quantile Plots of the Prediction Variance for Partially Replicated Central Composite Design

Umelo-Ibemere¹,

Amuji²

2015

IJSP

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Sometimes, it is not feasible to fully replicate the experimental units. When this happen, there is need for optimal replication of the experimental units to avoid bias. The prediction variance of two variations of the partially replicated central composite design (replicated cube plus one star and one cube plus replicated star) are compared using the quantile plots. These plots provide information about the prediction variance distribution on a sphere for comprehensive evaluation of the quality of the prediction variance. For face-centred (α = 1) and rotatable (α = F 1 4 ) central composite designs, the prediction variance of the one cube plus replicated star perform better than the replicated cube plus one star. Unlike the replicated cube plus one star, the quantile plots of the scaled prediction variance of the one cube plus replicated star depict near rotatability.

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Surrogate modeling-based multi-objective optimization for the integrated distillation processes

Wang

Zhang

et al. 2021

Chemical Engineering and Processing - Process Intensification

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Comparison of Prediction Capabilities of Partially Replicated Central Composite Designs in Cuboidal Region

Cited by 4 publications

References 8 publications

Optimal Prediction Variance Capabilities of Inscribed Central Composite Designs

Optimal Prediction Variance Capabilities of Inscribed Central Composite Designs

Quantile Plots of the Prediction Variance for Partially Replicated Central Composite Design

Surrogate modeling-based multi-objective optimization for the integrated distillation processes

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