Bayesian D-optimal supersaturated designs

Bradley Jones, Dennis K.J. Lin, Christopher J. Nachtsheim

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E (s2) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.

Original languageEnglish (US)
Pages (from-to)86-92
Number of pages7
JournalJournal of Statistical Planning and Inference
Volume138
Issue number1
DOIs
StatePublished - Jan 1 2008

Fingerprint

Supersaturated Design
D-optimal Design
Sample Size
D-optimality
Quality Improvement
Categorical
Experiment
Diagnostics
Experiments
Arbitrary
Design
Optimal design
Sample size
Factors

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Jones, Bradley ; Lin, Dennis K.J. ; Nachtsheim, Christopher J. / Bayesian D-optimal supersaturated designs. In: Journal of Statistical Planning and Inference. 2008 ; Vol. 138, No. 1. pp. 86-92.
@article{3dcb25aefea247e894c14be134428b3d,
title = "Bayesian D-optimal supersaturated designs",
abstract = "We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E (s2) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.",
author = "Bradley Jones and Lin, {Dennis K.J.} and Nachtsheim, {Christopher J.}",
year = "2008",
month = "1",
day = "1",
doi = "10.1016/j.jspi.2007.05.021",
language = "English (US)",
volume = "138",
pages = "86--92",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",
number = "1",

}

Bayesian D-optimal supersaturated designs. / Jones, Bradley; Lin, Dennis K.J.; Nachtsheim, Christopher J.

In: Journal of Statistical Planning and Inference, Vol. 138, No. 1, 01.01.2008, p. 86-92.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Bayesian D-optimal supersaturated designs

AU - Jones, Bradley

AU - Lin, Dennis K.J.

AU - Nachtsheim, Christopher J.

PY - 2008/1/1

Y1 - 2008/1/1

N2 - We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E (s2) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.

AB - We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E (s2) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.

UR - http://www.scopus.com/inward/record.url?scp=34548850048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548850048&partnerID=8YFLogxK

U2 - 10.1016/j.jspi.2007.05.021

DO - 10.1016/j.jspi.2007.05.021

M3 - Article

AN - SCOPUS:34548850048

VL - 138

SP - 86

EP - 92

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 1

ER -