Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this article, we define mixed predictor and stochastic restricted ridge predictor of partially linear mixed measurement error models by taking advantage of Kernel approximation. Under matrix mean square error criterion, we make the comparison of the superiorities the linear combinations of the new defined predictors. Then we investigate the asymptotic normality characteristics and the situation of the unknown covariance matrix of measurement errors. Finally, the study is ended with a Monte Carlo simulation study and COVID-19 data application.
Açıklama
Anahtar Kelimeler
Multicollinearity, Kernel Mixed Predictor, Kernel Stochastic Restricted Ridge Predictor, Asymptotic Normality, Partially Linear Mixed Measurement Error Models
Kaynak
Journal of Applied Statistics
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Kuran, Ö. ve Yalaz, S. (2023). Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19. Journal of Applied Statistics, 1-25.
