Searching for D-Efficient Equivalent-Estimation Second-Order Split-Plot Designs

Nguyen, Nam-Ky; Pham, Tung-Dinh

doi:10.1080/00224065.2015.11918106

Cited by 4 publications

(1 citation statement)

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“…Jones and Goos provided an algorithm for finding D‐efficient equivalent estimation second‐order split‐plot designs that outperformed those of Macharia and Goos and for cases in which the Macharia and Goos algorithm could not find equivalent estimation designs. Nguyen and Pham described an algorithm that produced D‐efficient equivalent estimation split‐plot designs by interchanging the subplot factor levels within each whole plot. They evaluated the design using the cases reported by Macharia and Goos and Jones and Goos …”

Section: Response Surface Split‐plot Designsmentioning

confidence: 99%

Response surface split‐plot designs: A literature review

Pineda

Simpson²,

Parker

2018

Quality & Reliability Eng

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The fundamental principles of experiment design are factorization, replication, randomization, and local control of error. In many industrial experiments, however, departure from these principles is commonplace. Often in our experiments, complete randomization is not feasible because factor level settings are hard, impractical, or inconvenient to change, or the resources available to execute under homogeneous conditions are limited. These restrictions in randomization result in split‐plot experiments. Also, we are often interested in fitting second‐order models, which lead to second‐order split‐plot experiments. Although response surface methodology has experienced a phenomenal growth since its inception, second‐order split‐plot design has received only modest attention relative to other topics during the same period. Many graduate textbooks either ignore or only provide a relatively basic treatise of this subject. The peer‐reviewed literature on second‐order split‐plot designs, especially with blocking, is scarce, limited in examples, and often provides limited or too general guidelines. This deficit of information leaves practitioners ill‐prepared to face the many challenges associated with these types of designs. This article seeks to provide an overview of recent literature on response surface split‐plot designs to help practitioners in dealing with these types of designs.

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Section: Response Surface Split‐plot Designsmentioning

confidence: 99%

Response surface split‐plot designs: A literature review

Pineda

Simpson²,

Parker

2018

Quality & Reliability Eng

View full text Add to dashboard Cite

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Staggered-Level Designs for Response Surface Modeling

Arnouts

Goos

2015

Journal of Quality Technology

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In industrial experiments, there are often restrictions in randomization caused by equipment and resource constraints, as well as budget and time restrictions. Next to the split-plot and the split-split-plot design, the staggered-level design is an interesting design option for experiments involving two hard-to-change factors. The staggered-level design allows both hard-to-change factors to be reset at different points in time, resulting in a typical staggering pattern of factor level resettings. It has been shown that, for two-level designs, this staggering pattern leads to statistical benefits in comparison to the split-plot and the split-split-plot design. In this paper, we investigate whether the benefits of the staggered-level design carry over to situations where the objective is to optimize a response, and where a second-order response surface model is in place. To this end, we study several examples of Dand I-optimal staggered-level response surface designs.

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