Bayesian Approaches to Models Robust and Models Discrimination Designs: A contribution of statistics to manufacturing, engineering, healthcare and business - Couverture souple

Agboto, Vincent

 
9783659660597: Bayesian Approaches to Models Robust and Models Discrimination Designs: A contribution of statistics to manufacturing, engineering, healthcare and business
Couverture souple
ISBN 10 : 3659660590 ISBN 13 : 9783659660597
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

Cost considerations will sometimes make it impractical to design experiments so that effects of all the factors could be estimated simultaneously. Therefore experimental designs are frequently constructed to estimate main effects and a few pre-specified interactions. A criticism frequently associated with the use of many optimality criteria is the specific reliance on an assumed statistical model. One way to deal with such a criticism may be to assume that instead the true model is an approximation of an unknown item of a known set of models. We consider a class of designs that are robust for change in model specification. We introduce an idea that uses the traditional Bayesian design method for parameter estimation and incorporates a discrete prior probability on the set of models of interest. We also introduce some model discrimination approaches that maximize the capability of the design for discriminating among competing models. The methodologies described in this book have the potential of improving significantly designs practices in manufacturing, engineering, healthcare and business. The author was invited twice at the University of Cambridge (UK) to present this work.

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Présentation de l'éditeur

Cost considerations will sometimes make it impractical to design experiments so that effects of all the factors could be estimated simultaneously. Therefore experimental designs are frequently constructed to estimate main effects and a few pre-specified interactions. A criticism frequently associated with the use of many optimality criteria is the specific reliance on an assumed statistical model. One way to deal with such a criticism may be to assume that instead the true model is an approximation of an unknown item of a known set of models. We consider a class of designs that are robust for change in model specification. We introduce an idea that uses the traditional Bayesian design method for parameter estimation and incorporates a discrete prior probability on the set of models of interest. We also introduce some model discrimination approaches that maximize the capability of the design for discriminating among competing models. The methodologies described in this book have the potential of improving significantly designs practices in manufacturing, engineering, healthcare and business. The author was invited twice at the University of Cambridge (UK) to present this work.

Biographie de l'auteur

Vincent Agboto is the Director of the Critical Care Research Center at HealthPartners in Minneapolis, MN. He was also the Director of Biostatistics at Meharry Medical College in Nashville, TN. He has published research articles and received funding from the NIH and the US Army. He received a M.S and a Ph.D in statistics from the University of MN.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
3659660590
ISBN 13 
9783659660597
Reliure
Broché
Nombre de pages
124
Coordonnées du fabricant
non disponible
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