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.

Optimal design generation: an approach based on discovery probability / Fontana, Roberto. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - STAMPA. - 30:4(2015), pp. 1231-1244. [10.1007/s00180-015-0562-1]

Optimal design generation: an approach based on discovery probability

FONTANA, ROBERTO
2015

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2588481
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