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


Ozmen A., Weber G. W.

6th Global Conference on Power Control and Optimization, Nevada, United States Of America, 6 - 08 August 2012, vol.1499, pp.337-343 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1499
  • Doi Number: 10.1063/1.4769011
  • City: Nevada
  • Country: United States Of America
  • Page Numbers: pp.337-343
  • Keywords: Robust Optimization, Conic Quadratic Programming, RCMARS, CGPLMs

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.