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Journals / Fundamentals of Contemporary Mathematical Sciences / 2026 / Volume: 5 Issue: 1

Generalized Kibria-Lukman Prediction Approximation in Linear Mixed Models

Özge Kuran(Dicle University Faculty of Science Department of Statistics Diyarbakır/TURKEY)0000-0001-5632-001X
Pages
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Publication Date
2024-01-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Lastly, the selection of the biasing parameter is given and to demonstrate the performance of our new de ned prediction method, the greenhouse gases data analysis is made.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the new suggested prediction method is the Kibria-Lukman's prediction approach under multicollinearity in linear mixed models and in this article, the generalized Kibria-Lukman estimator and predictor are introduced to combat multicollinearity problem.
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Key Findings & Data:The comparisons between the proposed generalized Kibria-Lukman estimator/predictor and several other estimators/predictors, namely the best linear unbiased estimator/predictor and Kibria-Lukman estimator/predictor are done by using the matrix mean square error criterion.
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Conclusion & Impact:Lastly, the selection of the biasing parameter is given and to demonstrate the performance of our new de ned prediction method, the greenhouse gases data analysis is made.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Linear mixed model · mean square error · generalized Kibria-Lukman predictor

Abstract

One of the new suggested prediction method is the Kibria-Lukman's prediction approach under multicollinearity in linear mixed models and in this article, the generalized Kibria-Lukman estimator and predictor are introduced to combat multicollinearity problem. The comparisons between the proposed generalized Kibria-Lukman estimator/predictor and several other estimators/predictors, namely the best linear unbiased estimator/predictor and Kibria-Lukman estimator/predictor are done by using the matrix mean square error criterion. Lastly, the selection of the biasing parameter is given and to demonstrate the performance of our new de ned prediction method, the greenhouse gases data analysis is made.

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Cite This Article

Kuran, Ö. (2024). Generalized Kibria-Lukman Prediction Approximation in Linear Mixed Models. Fundamentals of Contemporary Mathematical Sciences, Volume: 5 Issue: 1. https://doi.org/10.54974/fcmathsci.1263030

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