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Simulated Annealing Model Search for Subset Selection in Screening Experiments

Summary: [This abstract is based on the authors' abstract.] Typical subset selection methods do not work well with the large numbers of variables, small number of trials, and structural constraints of screening experiments. A new method of model selection using an intentionally nonconvergent stochastic search to generate a large number of well-fitting models is proposed. Actual selection of a model is treated as feature extraction problem using the generated set of models. Both a graphic method and an automatic method of selection are proposed.

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  • Topics: Statistics, Design of Experiments
  • Keywords: Facorial designs, Linear regression, Modeling, Selection, Stochastic models
  • Author: Wolters, Mark A. ; Bingham, Derek
  • Journal: Technometrics