Combating Multicollinearity: A New Two-Parameter Approach

Year 2023, Volume: 5 Issue: 1, 90 - 116, 30.06.2023

Janet Iyabo Idowu , Olasunkanmi James Oladapo , Abiola Timothy Owolabi , Kayode Ayinde , Oyinlade Akinmoju

https://doi.org/10.51541/nicel.1084768

Cited By: 4

Abstract

The ordinary least square (OLS) estimator is the Best Linear Unbiased Estimator (BLUE) when all linear regression model assumptions are valid. The OLS estimator, however, becomes inefficient in the presence of multicollinearity. Various one and two-parameter estimators have been proposed to circumvent the problem of multicollinearity. This paper presents a new twoparameter estimator called Liu-Kibria Lukman Estimator (LKL) estimator. The proposed estimator is compared theoretically and through Monte Carlo simulation with existing estimators such as the ordinary least square, ordinary ridge regression, Liu, Kibria-Lukman, and Modified Ridge estimators. The results show that the proposed estimator performs better than existing estimators considered in this study under some conditions, using the mean square error criterion. A real-life application to Portland cement and Longley datasets supported the theoretical and simulation results by giving the smallest mean square error compared to the existing estimators.

Keywords

LKL Estimator, OLS Estimator, Monte Carlo Simulation, Multicollinearity, Mean Square Error

Çoklu İç İlişki İle Mücadele: Yeni İki Parametre Yaklaşımı

Abstract

Regresyon analizinde tüm varsayımlarının sağlanması durumunda en küçük kareler (EKK) tahmin edicisi, en iyi doğrusal yansız tahmin edicidir. Fakat, EKK tahmin edicisi çoklu iç ilişki durumunda etkinliğini kaybetmektedir. Çoklu iç ilişki problemini çözmek için tek ve iki parametreli bazı tahmin ediciler önerilmiştir. Bu çalışmada, Liu-Kibria Lukman tahmin edicisi olarak adlandırılan yeni iki parametreli bir tahmin edici önerilmiştir. Önerilen yeni tahmin edici EKK, Ridge, Liu, Kibria-Lukman, Modifiye Ridge tahmin edicileri ile teorik olarak ve Monte Carlo simülasyon çalışması ile karşılaştırılmıştır. Yapılan çalışmalarda ele alınan tahmin ediciler için hata kareler ortalaması kriterine göre önerilen tahmin edicinin daha iyi performansa sahip olduğu gösterilmiştir. Portland çimento ve Longley veri setleri için yapılan gerçek veri uygulamasında elde edilen sonuçlar ile teorik karşılaştırma ve simülasyon çalışmasının sonuçları desteklenmiştir.

Keywords

LKL Tahmin Edicisi, EKK Tahmin Edicisi, Monte Carlo Simulasyon, Çoklu İç İlişki, Hata Kereler Ortalaması.

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