@article{958783,
      recid = {958783},
      author = {Özmen, Ayse,},
      title = {Robust optimization of spline models and complex  regulatory networks : theory, methods and applications /},
      pages = {1 online resource},
      abstract = {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.},
      url = {http://library.usi.edu/record/958783},
      doi = {https://doi.org/10.1007/978-3-319-30800-5},
}