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

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