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

Ay¿e Özmen

ISBN 10: 3319307991 / ISBN 13: 9783319307992

Edité par Springer International Publishing, 2016

A propos de cet article

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 9783319307992

Détails bibliographiques

• Titre: Robust Optimization of Spline Models and Complex Regulatory Networks : Theory, Methods and Applications
• Éditeur: Springer International Publishing
• Date de parution: 2016
• Langue: anglais
• ISBN à 10 chiffres: 3319307991
• ISBN à 13 chiffres: 9783319307992
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 14 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 14 millimètres de profondeur
• Poids: 401 grammes

A propos de ce titre

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.

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.