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Level-Screening Designs for factors with many levels

Brown, Philip J., Ridout, Martin S. (2016) Level-Screening Designs for factors with many levels. The Annals of Applied Statistics, 10 (2). 864 -883. ISSN 1932-6157. E-ISSN 1941-7330. (doi:10.1214/16-AOAS916)

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Official URL
http://dx.doi.org/10.1214/16-AOAS916

Abstract

We consider designs for f factors each at m levels, where f is small but m is large. Main effect designs with mf experimental points are presented. For two factors, two types of designs are investigated, termed sawtooth and dumbbell designs, based on a graphical representation. For three factors, cyclic sawtooth designs are considered. The paper seeks optimal and near optimal designs which involve factors with many levels but few observations. It also investigates issues of robustness when as much as one third of the data is structurally missing. An important area of application is in screening for drug discovery and we compare our designs with others using a published data set with two factors each with fifty levels, where the dumbbell design outperforms others and is an example of an inherently unbalanced design dominating more balanced designs.

Item Type: Article
DOI/Identification number: 10.1214/16-AOAS916
Uncontrolled keywords: Screening designs; lead optimisation in drug discovery; main effects; microarray loop designs; connectivity; identifiability; prediction and contrast variance.
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Philip J Brown
Date Deposited: 11 Aug 2016 20:36 UTC
Last Modified: 29 May 2019 17:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/56805 (The current URI for this page, for reference purposes)
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