Alternative Ridge Parameters in Linear Model

Yıl 2022, , 22 - 46, 30.06.2022

Kayode Ayinde , Emmanuel Adewuyi , Lukman Adewale Folaranmi

https://doi.org/10.51541/nicel.1042316

Cited By: 1

Öz

The ridge regression estimator produces efficient estimates than the Ordinary Least Square Estimator in a linear regression model that has multicollinearity problem. However, the efficiency of the ridge estimator depends on the choice of the ridge parameter, k. This parameter being the biasing parameter that shrinks the coefficient as it tends towards positive infinity needs to be chosen optimally to minimize the mean squared errors of the parameters. In this study, the ridge parameters are classified into different forms, various types and diverse kinds. These classifications resulted into proposing some other techniques of Ridge parameter estimation. Investigation of the existing and proposed ridge parameters were done by conducting Monte-Carlo experiments. Results from simulation study and reallife data application show that some newly proposed ridge parameters are among those that provide efficient estimates.

Anahtar Kelimeler

Linear regression, Ridge regression, Biasing parameters, Multicollinearity

Doğrusal Regresyonda Alternatif Ridge Parametreleri

Öz

Ridge regresyon tahmin edicisi çoklu iç ilişki problemi olan doğrusal regresyon modelinde En Küçük Kareler tahmin edicisinden daha etkin sonuçlar verir. Fakat, Ridge tahmin edicisinin performansı Ridge parametresinin seçimine bağlıdır. Ridge parametreleri farklı türlerde sınıflandırılmaktadır. Bu nedenle Ridge parametrelerinin tahmini için farklı teknikler önerilmiştir. Varolan ve yeni önerilen Ridge parametrelerinin karşılaştırılması için Monte Carlo simülasyon çalışması yapılmıştır. Simülasyon çalışması ve gerçek veri seti sonuçlarına bakıdığında önerilen Ridge parametresi tahmincilerinin etkin sonuçlar verdiği gösterilmiştir.

Anahtar Kelimeler

Doğrusal regresyon, Ridge regresyon, Çoklu iç ilişki, Yanlılık parametresi

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