Designs for generalized linear models with several variables and model uncertainty
Woods, D. C., Lewis, S. M., Eccleston, J. A. and Russell, K. G. (2006) Designs for generalized linear models with several variables and model uncertainty. Technometrics, 48, (2), 284-292. (doi:10.1198/004017005000000571)
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Description/Abstract
Standard factorial designs may sometimes be inadequate for
experiments that aim to estimate a generalized linear model, for
example, for describing a binary response in terms of several
variables. A method is proposed for finding exact designs for such
experiments which uses a criterion that allows for uncertainty in
the link function, the linear predictor or the model parameters,
together with a design search. Designs are assessed and compared
by simulation of the distribution of efficiencies relative to
locally optimal designs over a space of possible models. Exact
designs are investigated for two applications and their advantages
over factorial and central composite designs are demonstrated.
| Item Type: | Article |
|---|---|
| ISSN: | 1537-2723 (print) |
| Uncontrolled Keywords: | binary response, d-optimality, logistic regression, robust design, simulation |
| Subjects: | Q Science > QA Mathematics H Social Sciences > HA Statistics |
| Divisions: | University Structure - Pre August 2011 > School of Mathematics University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute University Structure - Pre August 2011 > School of Mathematics > Statistics |
| ePrint ID: | 15828 |
| Deposited On: | 14 Jun 2005 |
| Last Modified: | 01 Jun 2011 11:55 |
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