Furthermore, a numerical example is examined to compare these estimators under the prediction mean squared error criterion.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we introduce two linear convex combination estimators by using known estimators such as ordinary least squares, ridge and Liu estimators and examine the predictive performance of these estimators.
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Key Findings & Data:In this paper, we introduce two linear convex combination estimators by using known estimators such as ordinary least squares, ridge and Liu estimators and examine the predictive performance of these estimators.
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Conclusion & Impact:Furthermore, a numerical example is examined to compare these estimators under the prediction mean squared error criterion.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper, we introduce two linear convex combination estimators by using known estimators such as ordinary least squares, ridge and Liu estimators and examine the predictive performance of these estimators. Furthermore, a numerical example is examined to compare these estimators under the prediction mean squared error criterion.
Özet
Bu makalede, en küçük kareler, ridge ve Liu tahmin ediciler gibi bilinen tahmin edicilerle öngörü performansını karşılaştırmak için iki lineer konveks kombinasyon tahmin edicisi tanımlanmıştır. Ayrıca, öngörü hata kareleri ortalaması kriterine göre bu tahmin edicilerin karşılaştırılmaları bir sayısal örnek ile incelenmiştir.
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Cite This Article
KAÇIRANLAR, S., DAWOUD, I. A., KARAPINAR, D. (2022). Linear Convex Combination Estimators and Comparisons. Adıyaman Üniversitesi Fen Bilimleri Dergisi, Cilt: 11 - Sayı: 2, 302–320. https://ajindex.com/articles/428406
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