Kernel Liu prediction approach in partially linear mixed measurement error models
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor and Francis Ltd.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, we put forward ‘kernel Liu prediction approach’ instead of ‘kernel prediction approach’ under multicollinearity case in partially linear mixed measurement error model. We obtain 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 Liu biasing parameter via the Conceptual Prediction ((Formula presented.)) criterion. The asymptotic normality condition is examined and the unknown covariance matrix of measurement errors circumstance is derived. We study a numerical example together with a Monte Carlo simulation study to evaluate the performance of the kernel Liu prediction approach at the end of this paper.
Açıklama
Anahtar Kelimeler
Asymptotic normality, Kernel Liu prediction, Measurement error, Multicollinearity, Partially linear mixed model
Kaynak
Statistics
WoS Q Değeri
Q4
Scopus Q Değeri
Q4
Cilt
56
Sayı
6
Künye
Kuran, Ö. ve Yalaz, S. (2022). Kernel Liu prediction approach in partially linear mixed measurement error models, Statistics, 56(6), 1385-1408.
