ROBUST CONIC GENERALIZED PARTIAL LINEAR MODELS USING RCMARS METHOD - A ROBUSTIFICATION OF CGPLM
6th Global Conference on Power Control and Optimization, Nevada, Amerika Birleşik Devletleri, 6 - 08 Ağustos 2012, cilt.1499, ss.337-343, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 1499
- Doi Numarası: 10.1063/1.4769011
- Basıldığı Şehir: Nevada
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.337-343
- Anahtar Kelimeler: Robust Optimization, Conic Quadratic Programming, RCMARS, CGPLMs
- Orta Doğu Teknik Üniversitesi Adresli: Evet
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.