Kernel ridge prediction method in partially linear mixed measurement error model
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
In this article, a new kernel prediction method by using ridge regression approach is suggested to combat multicollinearity and the impacts of its existence on various views of partially linear mixed measurement error model. We derive the necessary and sufficient condition for the superiority of the linear combinations of the predictors in the sense of the matrix mean square error criterion and give the selection of the ridge biasing parameter. The asymptotic normality condition is investigated and the unknown covariance matrix of measurement errors circumstance is handled. A real data analysis together with a Monte Carlo simulation study is made to assess endorsement of the kernel ridge prediction method.
Açıklama
Anahtar Kelimeler
Asymptotic normality, Kernel ridge prediction, Measurement error, Multicollinearity, Partially linear mixed model
Kaynak
Communications in Statistics - Simulation and Computation
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
Early Access
Sayı
Künye
Kuran, Ö. ve Yalaz, S. (2022). Kernel ridge prediction method in partially linear mixed measurement error model. Communications in Statistics - Simulation and Computation, Early Access
