A New Kernel Prediction Approach Under Kibria‐Lukman Methodology for Partially Linear Mixed Measurement Error Models

ABSTRACT In this study, we propose a new kernel prediction approach based on the Kibria‐Lukman methodology to reduce the effects of multicollinearity in partially linear mixed measurement error models. The performance of the proposed kernel Kibria‐Lukman estimator/predictor is evaluated and compared with several existing alternatives, including the kernel, kernel ridge, and kernel Liu estimators/predictors, using the matrix mean square error criterion. Furthermore, we examine the asymptotic normality of the proposed method and address the case of an unknown covariance matrix of measurement errors. To support the theoretical findings, both a real earthquake dataset and an extensive Monte Carlo simulation study are analyzed.