J.N. Srivastava and experimental design

Ghosh, Subir; Dean, Angela

doi:10.1016/j.jspi.2012.09.007

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…A factor-based search already makes some restrictions on which models that can be fitted. However, any design found by choosing six columns from a 12-run PB design cannot guarantee the discrimination among models with three active factors having three main effects and three two-factor interactions, since at least 13 runs will be needed (see Cheng 29 and Morgan et al 30 ). Examples with six and seven factor NC designs where two different models with four active factors gave identical fit in the noiseless case is given in Section 5.4.…”

Section: Discussionmentioning

confidence: 99%

On the identification of active factors in nonregular two‐level designs with a small number of runs

Hamre

Tyssedal

2022

Quality & Reliability Eng

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Nonregular two-level designs are attractive screening designs due to their good projection properties and flexible run sizes. In particular, the 12-run Plackett-Burman (PB) design has become quite popular. However, existing methods struggle with the identification of active factors when the number of active factors exceeds the projectivity of the designs. This is especially the case when interactions are present, the variance is high and the number of runs is small. In this paper, we propose a method for analysing nonregular two-level designs that particularly addresses the issues above. It exploits the projection properties of designs and is here applied on the 12-run PB design and the 16-run no-confounding (NC) designs. In the construction of the method, the use of test-and penalty-based procedures are avoided. Instead, the number of allowed terms in a model is restricted. The effectiveness of the method and comparison between designs are evaluated by simulations for different scenarios. Ways to evaluate the reliability of the screening procedure are pointed out. An example with real data is given to demonstrate how one might perform the analysis in practice.

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Section: Discussionmentioning

confidence: 99%

On the identification of active factors in nonregular two‐level designs with a small number of runs

Hamre

Tyssedal

2022

Quality & Reliability Eng

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Supersaturated Designs With the Maximum Number of Factors for a Given Resolution-Rank

Gutman

Bulutoglu

White

2014

Journal of Statistical Theory and Practice

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General weighted optimality of designed experiments

Stallings

2015

Biometrika

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I would also like to extend my gratitude to Drs. Xinwei Deng, Klaus Hinkelmann, and Brad Jones, who were gracious enough to serve on my committee. Their advice and suggestions are greatly appreciated and I hope to work with them again in the future. Finally, I want to thank my family, especially my father, Wade, brother, Robert, and sister, Amy, and beloved partner, Sarah, who provided constant support during many personal challenges, including my mother's unexpected passing. I would like to dedicate this dissertation to my mother, Delores Tiller Stallings, who would have been immensely proud to see it completed. iii List of Tables 3.1 Weights assigned by W Hw in Example 3.2.1 to pairwise comparisons for treatments between and within treatment groups V 1 and V 2 with w 0 = 20.. .. . 3.2 Replication proportions for treatments of a given heft for A-optimal approximate CRD with respect to estimation of all non-normalized BP effects.. .. 3.3 A-optimal CRDs for estimation of all non-normalized BP effects for m = 3 factors. The last row indicates whether the design is a BA of strength 3.. . 3.4 A-optimal CRDs for estimation of all normalized BP effects for m = 3 factors. The last row indicates whether the design is a BA of strength 3.. .. .. . .

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J.N. Srivastava and experimental design

Cited by 3 publications

References 69 publications

On the identification of active factors in nonregular two‐level designs with a small number of runs

On the identification of active factors in nonregular two‐level designs with a small number of runs

Supersaturated Designs With the Maximum Number of Factors for a Given Resolution-Rank

General weighted optimality of designed experiments

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