RCMARS: Robustification of CMARS with different scenarios under polyhedral uncertainty set

Date

2011-12-01

Author

Ozmen, Ayse
Weber, Gerhard Wilhelm
Batmaz, İnci
Kropat, Erik

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Our recently developed CMARS is powerful in handling complex and heterogeneous data. We include into CMARS the existence of uncertainty about the scenarios. Indeed, data include noise in both output and input variables. Therefore, solutions of the optimization problem may reveal a remarkable sensitivity to perturbations in the parameters of the problem. The data uncertainty results in uncertain constraints and objective function. To overcome this difficulty, we refine our CMARS algorithm by a robust optimization technique proposed to cope with data uncertainty. In our previous study, we present the new robust CMARS (RCMARS) in theory and method and illustrate it with a numerical example. In this study, we present RCMARS results with different uncertainty scenarios for our numerical example.

Subject Keywords

Regression, Robust optimization, Robustness

URI

https://hdl.handle.net/11511/39681

Collections

Department of Statistics, Article