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, Amerika Birleşik Devletleri, 6 - 08 Ağustos 2012, cilt.1499, ss.337-343 identifier identifier

  • Cilt numarası: 1499
  • Doi Numarası: 10.1063/1.4769011
  • Basıldığı Şehir: Nevada
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.337-343

Özet

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.