Robust Optimization of Spline Models and Complex Regulatory Networks: Theory, Methods and Applications - Couverture souple
Synopsis
This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are implemented and explained in two-model regulatory systems that can be found in the financial sector and in the contexts of banking, environmental protection, system biology and medicine. The book provides necessary background information on multi-model regulatory networks, optimization and regression. It presents the theory of and approaches to robust (conic) multivariate adaptive regression splines - R(C)MARS – and robust (conic) generalized partial linear models – R(C)GPLM – under polyhedral uncertainty. Further, it introduces spline regression models for multi-model regulatory networks and interprets (C)MARS results based on different datasets for the implementation. It explains robust optimization in these models in terms of both the theory and methodology. In this context it studies R(C)MARS results with different uncertainty scenarios for a numerical example. Lastly, the book demonstrates the implementation of the method in a number of applications from the financial, energy, and environmental sectors, and provides an outlook on future research.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Ayşe Özmen has affiliation at Turkish Energy Foundation(TENVA)and Institute of Applied Mathematics of Middle East Technical University (METU), Ankara, Turkey. Her research is on OR, optimization, energy modelling, renewable energy systems, network modelling, regulatory networks, data mining. She received her Doctorate in Scientific Computing at Institute for Applied Mathematics at METU.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Robust Optimization of Spline Models and Complex Regulatory Networks
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Springer International Publishing Mai 2018, 2018
ISBN 10 : 3319808907
ISBN 13 : 9783319808901
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Robust Optimization of Spline Models and Complex Regulatory Networks: Theory, Methods and Applications (Contributions to Management Science)
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Robust Optimization of Spline Models and Complex Regulatory Networks
Edité par
Springer International Publishing, 2018
ISBN 10 : 3319808907
ISBN 13 : 9783319808901
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presentsnew methods of robust optimization to handle uncertainty and non-linearity incomplex regulatory networksProvidesguidance in the trade-off between accuracy and robustnessExemplifiesthe new methods in three detailed applications involving fina. N° de réf. du vendeur 448755873
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Robust Optimization of Spline Models and Complex Regulatory Networks
Edité par
Springer International Publishing, Springer Mai 2018, 2018
ISBN 10 : 3319808907
ISBN 13 : 9783319808901
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Robust Optimization of Spline Models and Complex Regulatory Networks : Theory, Methods and Applications
ISBN 10 : 3319808907
ISBN 13 : 9783319808901
Neuf
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces methods of robust optimization in multivariateadaptive regression splines (MARS) and Conic MARS in order to handleuncertainty and non-linearity. The proposed techniques are implemented andexplained in two-model regulatory systems that can be found in the financialsector and in the contexts of banking, environmental protection, system biologyand medicine. The book provides necessarybackground information on multi-model regulatory networks, optimizationand regression. It presents the theory of and approaches to robust (conic)multivariate adaptive regression splines - R(C)MARS - and robust (conic)generalized partial linear models - R(C)GPLM - under polyhedral uncertainty. Further,it introduces spline regression models for multi-model regulatory networks andinterprets (C)MARS results based on different datasets for the implementation.It explains robust optimization in these models in terms of both the theory andmethodology. In this context it studies R(C)MARS results with differentuncertainty scenarios for a numerical example. Lastly, the book demonstratesthe implementation of the method in a number of applications from thefinancial, energy, and environmental sectors, and provides an outlook on futureresearch. N° de réf. du vendeur 9783319808901
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