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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Robust Optimization of Spline Models and Complex Regulatory Networks/ by Ayşe Özmen.
其他題名:	Theory, Methods and Applications /
作者:	Özmen, Ayşe.
面頁冊數:	XII, 139 p. 22 illus., 20 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Operations research. -
電子資源:	https://doi.org/10.1007/978-3-319-30800-5
ISBN:	9783319308005

Robust Optimization of Spline Models and Complex Regulatory Networks = Theory, Methods and Applications /
Özmen, Ayşe.

Robust Optimization of Spline Models and Complex Regulatory NetworksTheory, Methods and Applications /[electronic resource] :by Ayşe Özmen. - 1st ed. 2016. - XII, 139 p. 22 illus., 20 illus. in color.online resource. - Contributions to Management Science,1431-1941. - Contributions to Management Science,.

Introduction -- Mathematical Methods Used -- New Robust Analytic Tools -- Spline Regression Models for Complex Multi-Model Regulatory Networks -- Robust Optimization in Spline Regression Models for Regulatory Networks Under Polyhedral Uncertainty -- Real-World Application with Our Robust Tools -- Conclusion and Outlook. .

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.

ISBN: 9783319308005

Standard No.: 10.1007/978-3-319-30800-5doiSubjects--Topical Terms:

573517
Operations research.

LC Class. No.: HD30.23

Dewey Class. No.: 658.40301

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