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.
|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
|Date Deposited:||10 May 2010 09:52|
|Last Modified:||01 Jun 2011 05:49|
|Contributors:||Woods, D.C. (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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