Open Access
June 2016 Level-screening designs for factors with many levels
Philip J. Brown, Martin S. Ridout
Ann. Appl. Stat. 10(2): 864-883 (June 2016). DOI: 10.1214/16-AOAS916


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.


Download Citation

Philip J. Brown. Martin S. Ridout. "Level-screening designs for factors with many levels." Ann. Appl. Stat. 10 (2) 864 - 883, June 2016.


Received: 1 December 2014; Revised: 1 February 2016; Published: June 2016
First available in Project Euclid: 22 July 2016

zbMATH: 06625672
MathSciNet: MR3528363
Digital Object Identifier: 10.1214/16-AOAS916

Keywords: connectivity , Identifiability , lead optimization in drug discovery , main effects , microarray loop designs , prediction and contrast variance , Screening designs

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 2 • June 2016
Back to Top