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.
Henderson's method Kernel estimator Kernel predictor Partially linear mixed model Semiparametric models
Birincil Dil | İngilizce |
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Bölüm | Natural Sciences |
Yazarlar | |
Yayımlanma Tarihi | 30 Eylül 2020 |
Gönderilme Tarihi | 7 Ocak 2020 |
Kabul Tarihi | 15 Haziran 2020 |
Yayımlandığı Sayı | Yıl 2020Cilt: 41 Sayı: 3 |