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ROBUST CONIC GENERALIZED PARTIAL LINEAR MODELS USING RCMARS METHOD - A ROBUSTIFICATION OF CGPLM
Date
2012-08-08
Author
Ozmen, Ayse
Weber, Gerhard Wilhelm
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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