ROBUST CONIC GENERALIZED PARTIAL LINEAR MODELS USING RCMARS METHOD - A ROBUSTIFICATION OF CGPLM

Date

2012-08-08

Author

Ozmen, Ayse
Weber, Gerhard Wilhelm

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GPLM is a combination of two different regression models each of which is used to apply on different parts of the data set. It is also adequate to high dimensional, non-normal and nonlinear data sets having the flexibility to reflect all anomalies effectively. In our previous study, Conic GPLM (CGPLM) was introduced using CMARS and Logistic Regression. According to a comparison with CMARS, CGPLM gives better results. In this study, we include the existence of uncertainty in the future scenarios into CMARS and linear/logit regression part in CGPLM and robustify it with robust optimization which is dealt with data uncertainty. Moreover, we apply RCGPLM on a small data set as a numerical experience from the financial sector.

Subject Keywords

CGPLMs, RCMARS, Conic Quadratic Programming, Robust optimization

URI

https://hdl.handle.net/11511/57097

DOI

https://doi.org/10.1063/1.4769011

Collections

Graduate School of Applied Mathematics, Conference / Seminar

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Citation Formats

A. Ozmen and G. W. Weber, “ROBUST CONIC GENERALIZED PARTIAL LINEAR MODELS USING RCMARS METHOD - A ROBUSTIFICATION OF CGPLM,” 2012, vol. 1499, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57097.