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Journals / Nicel Bilimler Dergisi / 2024 / Cilt: 6 Sayı: 2

Comparison of Restricted and Unrestricted estimators with a Monte-Carlo Simulation Study

Monte-Carlo Simülasyon Çalışması ile Sınırlı ve Sınırsız Tahmin Edicilerin Karşılaştırılması

Israa Shaltoot0000-0002-5428-397X
Nihal İnce0000-0001-6684-5848
Sevil Şentürk(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0000-0002-9503-7388
Pages
228–273
Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, it was decided that the most effective estimators are the restricted biased estimators when it comes to the state of multicollinearity.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study deals with the problem of multicollinearity in the linear regression model.
📊
Key Findings & Data:Estimators' performance is compared according to matrix mean square error and scalar mean square error.
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Conclusion & Impact:As a result, it was decided that the most effective estimators are the restricted biased estimators when it comes to the state of multicollinearity.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Çoklu doğrusallık · Ortalama karesel hata · Yanlı tahminciler

Abstract

This study deals with the problem of multicollinearity in the linear regression model. Restricted and unrestricted parameter estimates are chosen among biased estimators to be studied and compared as two corresponding groups, with the aim of identifying which group gives better parameter estimates in the case of multicollinearity. Estimators' performance is compared according to matrix mean square error and scalar mean square error. Proceeding from this, it has been shown that, in the sense of Scalar Mean Square Error (SMSE), the Restricted Ridge regression (RRR) estimator outperforms all constrained and unconstrained estimators, while the Ridge regression is superior to the unconstrained set of estimators. A real-life application and Monte-Carlo simulation study are conducted to compare the performance of restricted and unrestricted estimators. As a result, it was decided that the most effective estimators are the restricted biased estimators when it comes to the state of multicollinearity.

Özet

Bu çalışma, doğrusal regresyon modelinde çoklu doğrusallık sorununu ele almaktadır. Çoklu doğrusal bağlantı durumunda hangi grubun daha iyi parametre tahminleri verdiğini belirlemek amacıyla, incelenecek ve karşılaştırılacak yanlı tahmin ediciler arasından kısıtlı ve kısıtlı olmayan parametre tahminleri iki grup olarak seçilmiştir. Tahmin edicilerin performansı matris ortalama kare hatası ve skaler ortalama kare hatasına göre karşılaştırılmıştır. Buradan hareketle, Skaler Ortalama Kare Hata (SMSE) anlamında, Kısıtlı Ridge regresyonu (RRR) tahmin edicisinin tüm kısıtlı ve kısıtlı olmayan tahmin edicilerden daha iyi performans gösterdiği, Ridge regresyonunun ise kısıtlı olmayan tahmin ediciler kümesinden daha üstün olduğu gösterilmiştir. Kısıtlı ve kısıtlı olmayan tahmin edicilerin performansını karşılaştırmak için gerçek hayattan bir uygulama ve Monte-Carlo simülasyon çalışması yapılmıştır. Sonuç olarak, çoklu doğrusal bağlantı durumu söz konusu olduğunda en etkin tahmin edicilerin kısıtlı yanlı tahmin ediciler olduğuna karar verilmiştir.

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Cite This Article

Shaltoot, I., İnce, N., Şentürk, S. (2024). Comparison of Restricted and Unrestricted estimators with a Monte-Carlo Simulation Study. Nicel Bilimler Dergisi, Cilt: 6 Sayı: 2, 228–273. https://doi.org/10.51541/nicel.1375616

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