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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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
Digital Object Identifier (DOI): doi:10.1080/00949650802445367
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
ePrint ID: 151261
Accepted Date and Publication Date:
January 2010Published
Date Deposited: 10 May 2010 09:52
Last Modified: 31 Mar 2016 13:23
PLATFORM: End-to-End pipeline for chemical information: from the laboratory to literature and back again
Funded by: EPSRC (EP/C008863/1)
April 2005 to June 2010

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