Kernel ridge prediction method in partially linear mixed measurement error model

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.

Automated summary

This article proposes a new kernel prediction method using ridge regression to address the issue of multicollinearity and its impact on various aspects of the partially linear mixed measurement error model. The article provides theoretical analysis and empirical evaluation to assess the feasibility of the method.