1. Robust conic generalized partial linear models using RCMARS method - A robustification of CGPLM.
- Author
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Özmen, Ayşe and Weber, Gerhard Wilhelm
- Subjects
- *
ROBUST optimization , *QUADRATIC programming , *LINEAR statistical models , *LOGISTIC regression analysis , *ALGORITHMS - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2012
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