Robust designs for binary data: applications of simulated annealing
Woods, D.C. (2010) Robust designs for binary data: applications of simulated annealing. Journal of Statistical Computation and Simulation, 80, (1), 29-41. (doi:10.1080/00949650802445367).
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Description/Abstract
When the aim of an experiment is the estimation of a generalized linear model (GLM), standard designs from linear model theory may prove inadequate. This paper describes a flexible approach for finding designs for experiments to estimate GLMs through the use of D-optimality and a simulated annealing algorithm. A variety of uncertainties in the model can be incorporated into the design search, including the form of the linear predictor, through use of a robust design-selection criterion and a postulated model space. New methods appropriate for screening experiments and the incorporation of correlations between possible model parameters are described using examples. An updating formula for D-optimality under a GLM is presented, which improves the computational efficiency of the search.
| Item Type: | Article |
|---|---|
| ISSNs: | 0094-9655 (print) |
| Keywords: | generalized linear models, optimal design, prior information, screening experiments, simulation |
| Subjects: | Q Science > Q Science (General) |
| Divisions: | University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute |
| Item ID: | 151261 |
| Date Deposited: | 10 May 2010 09:52 |
| Last Modified: | 01 Jun 2011 05:49 |
| Contributors: | Woods, D.C. (Author) |
| Date: | January 2010 |
| Status: | Published |
| URI: | http://eprints.soton.ac.uk/id/eprint/151261 |
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