Bayesian D-optimal design issues for binomial generalized linear model screening designs

Edgar Hassler, Douglas Montgomery, Rachel T. Silvestrini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bayesian D-optimal designs have become computationally feasible to construct for simple prior distributions. Some parameter values give rise to models that have little utility to the practitioner for effect screening. For some generalized linear models such as the binomial, inclusion of such models can cause the optimal design to spread out toward the boundary of the design space. This can reduce the D-efficiency of the design over much of the parameter space and result in the Bayesian D-optimal criterion's divergence from the concerns of a practitioner designing a screening experiment.

Original languageEnglish (US)
Title of host publicationFrontiers in Statistical Quality Control 10
PublisherKluwer Academic Publishers
Pages337-353
Number of pages17
Volume11
ISBN (Print)9783319123547
DOIs
StatePublished - 2015
Event11th International Workshop on Intelligent Statistical Quality Control, 2013 - Sydney, Australia
Duration: Aug 20 2013Aug 23 2013

Other

Other11th International Workshop on Intelligent Statistical Quality Control, 2013
CountryAustralia
CitySydney
Period8/20/138/23/13

Fingerprint

Screening
Optimal design
Experiments

Keywords

  • Challenger data set
  • Confidence intervals
  • Non-linear designs

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Hassler, E., Montgomery, D., & Silvestrini, R. T. (2015). Bayesian D-optimal design issues for binomial generalized linear model screening designs. In Frontiers in Statistical Quality Control 10 (Vol. 11, pp. 337-353). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-12355-4_20

Bayesian D-optimal design issues for binomial generalized linear model screening designs. / Hassler, Edgar; Montgomery, Douglas; Silvestrini, Rachel T.

Frontiers in Statistical Quality Control 10. Vol. 11 Kluwer Academic Publishers, 2015. p. 337-353.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hassler, E, Montgomery, D & Silvestrini, RT 2015, Bayesian D-optimal design issues for binomial generalized linear model screening designs. in Frontiers in Statistical Quality Control 10. vol. 11, Kluwer Academic Publishers, pp. 337-353, 11th International Workshop on Intelligent Statistical Quality Control, 2013, Sydney, Australia, 8/20/13. https://doi.org/10.1007/978-3-319-12355-4_20
Hassler E, Montgomery D, Silvestrini RT. Bayesian D-optimal design issues for binomial generalized linear model screening designs. In Frontiers in Statistical Quality Control 10. Vol. 11. Kluwer Academic Publishers. 2015. p. 337-353 https://doi.org/10.1007/978-3-319-12355-4_20
Hassler, Edgar ; Montgomery, Douglas ; Silvestrini, Rachel T. / Bayesian D-optimal design issues for binomial generalized linear model screening designs. Frontiers in Statistical Quality Control 10. Vol. 11 Kluwer Academic Publishers, 2015. pp. 337-353
@inproceedings{92585cbf0d1042199518e0825d5f9f17,
title = "Bayesian D-optimal design issues for binomial generalized linear model screening designs",
abstract = "Bayesian D-optimal designs have become computationally feasible to construct for simple prior distributions. Some parameter values give rise to models that have little utility to the practitioner for effect screening. For some generalized linear models such as the binomial, inclusion of such models can cause the optimal design to spread out toward the boundary of the design space. This can reduce the D-efficiency of the design over much of the parameter space and result in the Bayesian D-optimal criterion's divergence from the concerns of a practitioner designing a screening experiment.",
keywords = "Challenger data set, Confidence intervals, Non-linear designs",
author = "Edgar Hassler and Douglas Montgomery and Silvestrini, {Rachel T.}",
year = "2015",
doi = "10.1007/978-3-319-12355-4_20",
language = "English (US)",
isbn = "9783319123547",
volume = "11",
pages = "337--353",
booktitle = "Frontiers in Statistical Quality Control 10",
publisher = "Kluwer Academic Publishers",

}

TY - GEN

T1 - Bayesian D-optimal design issues for binomial generalized linear model screening designs

AU - Hassler, Edgar

AU - Montgomery, Douglas

AU - Silvestrini, Rachel T.

PY - 2015

Y1 - 2015

N2 - Bayesian D-optimal designs have become computationally feasible to construct for simple prior distributions. Some parameter values give rise to models that have little utility to the practitioner for effect screening. For some generalized linear models such as the binomial, inclusion of such models can cause the optimal design to spread out toward the boundary of the design space. This can reduce the D-efficiency of the design over much of the parameter space and result in the Bayesian D-optimal criterion's divergence from the concerns of a practitioner designing a screening experiment.

AB - Bayesian D-optimal designs have become computationally feasible to construct for simple prior distributions. Some parameter values give rise to models that have little utility to the practitioner for effect screening. For some generalized linear models such as the binomial, inclusion of such models can cause the optimal design to spread out toward the boundary of the design space. This can reduce the D-efficiency of the design over much of the parameter space and result in the Bayesian D-optimal criterion's divergence from the concerns of a practitioner designing a screening experiment.

KW - Challenger data set

KW - Confidence intervals

KW - Non-linear designs

UR - http://www.scopus.com/inward/record.url?scp=84946047284&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946047284&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-12355-4_20

DO - 10.1007/978-3-319-12355-4_20

M3 - Conference contribution

AN - SCOPUS:84946047284

SN - 9783319123547

VL - 11

SP - 337

EP - 353

BT - Frontiers in Statistical Quality Control 10

PB - Kluwer Academic Publishers

ER -