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Dergiler / Gazi University Journal of Science / 2017 / Cilt: 30 - Sayı: 4

A REVIEW ON SHRINKAGE PARAMETERS IN RIDGE REGRESSION

Esra Gökpınar
Meral EBEGİL
Fikri Gökpınar
Sayfa
565–582
Yayın Tarihi
2017-12-11
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Also the estimators of shrinkage parameter are compared via simulation.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In the regression analysis, it is desired that no multicollinearity between the independent (explanatory) variables exists.
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Temel Bulgular & Veri:In the cases where this is not achieved, the use of Least Square (LS) estimation method leads to mismodelling.
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Sonuç & Katkı:Also the estimators of shrinkage parameter are compared via simulation.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Ridge Estimator · Mean Square Error · Optimum Ridge Parameter

Abstract

In the regression analysis, it is desired that no multicollinearity between the independent (explanatory) variables exists. In the cases where this is not achieved, the use of Least Square (LS) estimation method leads to mismodelling. Some methods have been developed to solve this problem; one of which is the ‘biased estimation method’. When there exists collinearity, selection of the shrinkage parameter is important. In this study, a test statistics for Ridge estimator that is kind of shrinkage biased estimators was investigated. Also the estimators of shrinkage parameter are compared via simulation.

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Bu Makaleye Atıf Yap

Gökpınar, E., EBEGİL, M., Gökpınar, F. (2017). A REVIEW ON SHRINKAGE PARAMETERS IN RIDGE REGRESSION. Gazi University Journal of Science, Cilt: 30 - Sayı: 4, 565–582. https://ajindex.com/articles/254901

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Kaynakça (17)

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