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Factorial and response surface designs robust to missing observations

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Factorial and response surface designs robust to missing observations. / da Silva, Marcelo A.; Gilmour, Steven G.; Trinca, Luzia A.

In: COMPUTATIONAL STATISTICS AND DATA ANALYSIS, Vol. 113, 10.1016/j.csda.2016.05.023, 09.2017, p. 261-272.

Research output: Contribution to journalArticle

Harvard

da Silva, MA, Gilmour, SG & Trinca, LA 2017, 'Factorial and response surface designs robust to missing observations', COMPUTATIONAL STATISTICS AND DATA ANALYSIS, vol. 113, 10.1016/j.csda.2016.05.023, pp. 261-272. https://doi.org/10.1016/j.csda.2016.05.023

APA

da Silva, M. A., Gilmour, S. G., & Trinca, L. A. (2017). Factorial and response surface designs robust to missing observations. COMPUTATIONAL STATISTICS AND DATA ANALYSIS, 113, 261-272. [10.1016/j.csda.2016.05.023]. https://doi.org/10.1016/j.csda.2016.05.023

Vancouver

da Silva MA, Gilmour SG, Trinca LA. Factorial and response surface designs robust to missing observations. COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 2017 Sep;113:261-272. 10.1016/j.csda.2016.05.023. https://doi.org/10.1016/j.csda.2016.05.023

Author

da Silva, Marcelo A. ; Gilmour, Steven G. ; Trinca, Luzia A. / Factorial and response surface designs robust to missing observations. In: COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 2017 ; Vol. 113. pp. 261-272.

Bibtex Download

@article{54520afdda8144429a12be298f82addb,
title = "Factorial and response surface designs robust to missing observations",
abstract = "Compound optimum design criteria which allow pure error degrees of freedom may produce designs that break down when even a single run is missing, if the number of experimental units is small. The inclusion, in the compound criteria, of a measure of leverage uniformity is proposed in order to produce designs that are more robust to missing observations. By appropriately choosing the weights of each part of the criterion, robust designs are obtained that are also highly efficient in terms of other properties. Applications to various experimental setups show the advantages of the new methods.",
author = "{da Silva}, {Marcelo A.} and Gilmour, {Steven G.} and Trinca, {Luzia A.}",
year = "2017",
month = sep,
doi = "10.1016/j.csda.2016.05.023",
language = "English",
volume = "113",
pages = "261--272",
journal = "COMPUTATIONAL STATISTICS AND DATA ANALYSIS",
issn = "0167-9473",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Factorial and response surface designs robust to missing observations

AU - da Silva, Marcelo A.

AU - Gilmour, Steven G.

AU - Trinca, Luzia A.

PY - 2017/9

Y1 - 2017/9

N2 - Compound optimum design criteria which allow pure error degrees of freedom may produce designs that break down when even a single run is missing, if the number of experimental units is small. The inclusion, in the compound criteria, of a measure of leverage uniformity is proposed in order to produce designs that are more robust to missing observations. By appropriately choosing the weights of each part of the criterion, robust designs are obtained that are also highly efficient in terms of other properties. Applications to various experimental setups show the advantages of the new methods.

AB - Compound optimum design criteria which allow pure error degrees of freedom may produce designs that break down when even a single run is missing, if the number of experimental units is small. The inclusion, in the compound criteria, of a measure of leverage uniformity is proposed in order to produce designs that are more robust to missing observations. By appropriately choosing the weights of each part of the criterion, robust designs are obtained that are also highly efficient in terms of other properties. Applications to various experimental setups show the advantages of the new methods.

U2 - 10.1016/j.csda.2016.05.023

DO - 10.1016/j.csda.2016.05.023

M3 - Article

VL - 113

SP - 261

EP - 272

JO - COMPUTATIONAL STATISTICS AND DATA ANALYSIS

JF - COMPUTATIONAL STATISTICS AND DATA ANALYSIS

SN - 0167-9473

M1 - 10.1016/j.csda.2016.05.023

ER -

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