Title
Minimax Distance Designs In Two-Level Factorial Experiments
Keywords
62K05; Bayesian design; Computer experiments; Design optimality criteria; Two-level fractional factorial design
Abstract
A minimax distance criterion was set forth in Johnson et al. (1990) for the purpose of selection among experimental designs. Unlike the usual design criteria such as D-, E- or G-optimality, minimax distance presumes no underlying model and, in turn, is not concerned with the rank of an associated design matrix. In situations where either the model is unknown or it is not possible to run enough experiments to estimate all parameters of an assumed model, this criterion is considered as a viable tool in the task of design selection. This paper deals with the design space associated with n factors, each of which can take two levels. We exhibit minimax distance designs that compare favorably with designs chosen to do well on classical grounds. © 1995.
Publication Date
4-1-1995
Publication Title
Journal of Statistical Planning and Inference
Volume
44
Issue
2
Number of Pages
249-263
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/0378-3758(94)00047-Y
Copyright Status
Unknown
Socpus ID
0041541641 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0041541641
STARS Citation
John, P. W.M.; Johnson, M. E.; and Moore, L. M., "Minimax Distance Designs In Two-Level Factorial Experiments" (1995). Scopus Export 1990s. 2024.
https://stars.library.ucf.edu/scopus1990/2024