Robust Optimization of Spline Models and Complex Regulatory Networks / Contributions to Management Science (PDF)
Theory, Methods and Applications
(Sprache: Englisch)
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...
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...
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Produktinformationen zu „Robust Optimization of Spline Models and Complex Regulatory Networks / Contributions to Management Science (PDF)“
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.
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.
Autoren-Porträt von Ayse Özmen
Ayse Özmen has affiliation at Turkish EnergyFoundation(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.
Bibliographische Angaben
- Autor: Ayse Özmen
- 2016, 1st ed. 2016, 139 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319308009
- ISBN-13: 9783319308005
- Erscheinungsdatum: 11.05.2016
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 2.83 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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