On foundations of estimation for nonparametric regression with continuous optimization
[ X ]
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IGI Global
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The aim of parametric regression models like linear regression and nonlinear regression are to produce a reasonable relationship between response and independent variables based on the assumption of linearity and predetermined nonlinearity in the regression parameters by finite set of parameters. Nonparametric regression techniques are widely-used statistical techniques, and they not only relax the assumption of linearity in the regression parameters, but they also do not need a predetermined functional form as nonlinearity for the relationship between response and independent variables. It is capable of handling higher dimensional problem and sizes of sample than regression that considers parametric models because the data should provide both the model building and the model estimates. For this purpose, firstly, PRSS problems for MARS, ADMs, and CR will be constructed. Secondly, the solution of the generated problems will be obtained with CQP, one of the famous methods of convex optimization, and these solutions will be called CMARS, CADMs, and CKR, respectively.
Açıklama
Anahtar Kelimeler
Kaynak
Handbook of Research on Big Data Clustering and Machine Learning
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Taylan, P. (2019). On foundations of estimation for nonparametric regression with continuous optimization. Handbook of Research on Big Data Clustering and Machine Learning, 177-203.
