Farklı Yanlılık Parametreleri İçin Ridge GM Tahmin Edicilerinin Performanslarının Karşılaştırılması

Year 2021, , 203 - 216, 30.06.2021

Melike Işılar , Y. Murat Bulut

https://doi.org/10.35193/bseufbd.877176

Cited By: 2

Abstract

Çoklu lineer regresyon modelinde yaygın olarak karşılaşılan problemler çoklu iç ilişki ve aykırı değer problemleridir. Bu iki problemin eş anlı çözümleri için literatürde sağlam yanlı tahmin ediciler üzerine pek çok çalışma mevcuttur. Bu tahmin edicilerden en yaygın kullanılanları sağlam Ridge tahmin edicileridir. Yanlılık parametresine bağlı olan Ridge tahmin edicisine ilişkin günümüzde de pek çok çalışma yapılmaktadır. Yapılan çalışmalarda yanlılık parametresinin performansı klasik Ridge tahmin edicisinde incelenmektedir. Bu çalışmada her iki değişkende de aykırı değer olması ve çoklu iç ilişki probleminin ortak çözümü için önerilmiş olan Ridge GM tahmin edicisinde literatürde daha önce önerilmiş olan yanlılık parametrelerinin performansları simülasyon çalışması ve gerçek veri örneği üzerinde incelenmiştir.

Keywords

Lineer Regresyon Modeli, Çoklu İç İlişki, Aykırı Değer, Ridge Regresyon, Yanlılık Parametresi

Supporting Institution

Eskişehir Osmangazi Üniversitesi

Project Number

2020-19A102

Thanks

Bu çalışma, Eskişehir Osmangazi Üniversitesi Bilimsel Araştırma Projeleri (ESOGÜBAP) Komisyonu tarafından 2020-19A102 nolu proje olarak desteklenmiştir.

Comparison of the Performances of Ridge GM Estimators for Different Biased Parameters

Abstract

In the multiple linear regression model, commonly encountered problems are multicollinearity and outlier problems. There are many studies about robust biased estimators in the literature to solve these problems simultaneously. The most commonly used of these estimators are robust Ridge estimators. Many studies are still carried out on the Ridge estimator, which depends on the biasing parameter. The performances of the previously proposed biased parameters were compared in the classical Ridge estimator. In this study, we have compared the performances of the biasing parameters for the robust Ridge GM estimators based on the simulation and real data studies.

Keywords

Linear Regression Model, Multicollinearity, Outlier, Ridge Regression, Biasing Parameter

Project Number

2020-19A102

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