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

Özge Kuran; Seçil Yalaz

doi:10.17776/csj.671812

EN

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 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.

Keywords

References

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Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Özge Kuran ^*
0000-0001-5632-001X
Türkiye

Seçil Yalaz
0000-0001-7283-9225
Türkiye

Publication Date

September 30, 2020

Submission Date

January 7, 2020

Acceptance Date

June 15, 2020

Published in Issue

Year 1970 Volume: 41 Number: 3

DOI

https://doi.org/10.17776/csj.671812

IZ

https://izlik.org/JA98LG24EE

Cite

RIS / Bibtex

APA

Kuran, Ö., & Yalaz, S. (2020). Henderson’s method approach to Kernel prediction in partially linear mixed models. Cumhuriyet Science Journal, 41(3), 571-579. https://doi.org/10.17776/csj.671812