Optimal design generation: an approach based on discovery probability

Roberto Fontana ()

Computational Statistics, 2015, vol. 30, issue 4, 1244 pages

Abstract: Efficient algorithms for searching for optimal saturated designs for sampling experiments are widely available. They maximize a given efficiency measure (such as D-optimality) and provide an optimum design. Nevertheless, they do not guarantee a global optimal design. Indeed, they start from an initial random design and find a local optimal design. If the initial design is changed the optimum found will, in general, be different. A natural question arises. Should we stop at the design found or should we run the algorithm again in search of a better design? This paper uses very recent methods and software for discovery probability to support the decision to continue or stop the sampling. A software tool written in SAS has been developed. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Design of experiments; Optimal designs; Unobserved species; Discovery probability (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s00180-015-0562-1

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