Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19

Abstract

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.

Keywords:

• Multicollinearity
• Kernel mixed predictor
• Kernel stochastic restricted ridge predictor
• asymptotic normality
• partially linear mixed measurement error models

Disclosure statement

No potential conflict of interest was reported by the author(s).