Henderson's method approach to Kernel prediction in partially linear mixed models

Abstract

In this article, we propose Kernel prediction in partially linear mixed models by usingHenderson's method approach. We derive the Kernel estimator and the Kernel predictor viathe mixed model equations (MMEs) of Henderson's that they give the best linear unbiasedestimation (BLUE) of the fixed effects parameters and the nonparametric functioncomputationally easier and the best linear unbiased prediction (BLUP) of the random effectsparameters as by-products. Additionally, asymptotic property of the Kernel estimator isinvestigated. A Monte Carlo simulation study is supported to illustrate the performance ofKernel prediction in partially linear mixed models and then, we finalize the article with thehelp of conclusion and discussion part to summarize the findings.