Robust Optimization of Spline Models and Complex Regulatory Networks: Theory, Methods and Applications - Rilegato

Libro 88 di 214: Contributions to Management Science

Ozmen, Ayse

 
9783319307992: Robust Optimization of Spline Models and Complex Regulatory Networks: Theory, Methods and Applications
Rilegato
ISBN 10: 3319307991 ISBN 13: 9783319307992
Casa editrice: Springer Verlag, 2016

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Ayse Özmen has affiliation at Turkish EnergyFoundation(TENVA)and Institute of Applied Mathematics of Middle East TechnicalUniversity (METU), Ankara, Turkey. Her research is on OR, optimization, energymodelling, renewable energy systems, network modelling, regulatory networks, datamining. She received her Doctorate in Scientific Computing at Institute forApplied Mathematics at METU. 

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Verlag
Data di pubblicazione
2016
Lingua
Inglese
ISBN 10 
3319307991
ISBN 13 
9783319307992
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
152
Contatto del produttore
non disponibile
Persona responsabile
CMN Printpool Inh. Mathis Neumann
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Hamburg
Germania

Altre edizioni note dello stesso titolo

9783319808901: Robust Optimization of Spline Models and Complex Regulatory Networks: Theory, Methods and Applications

Edizione in evidenza

ISBN 10:  3319808907 ISBN 13:  9783319808901
Casa editrice: Springer, 2018
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