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Journals / Gazi University Journal of Science / 2026 / Volume: 39 Issue: 2

Simulated Annealing Algorithm Based Ridge Estimator

Gizem İklil Kocasoy(GAZİ ÜNİVERSİTESİ, FEN FAKÜLTESİ)0000-0002-0068-5471
Meral Ebegil(GAZİ ÜNİVERSİTESİ, FEN FAKÜLTESİ)0000-0003-4798-3422
Muhlis Özdemir(GAZİ ÜNİVERSİTESİ, FEN FAKÜLTESİ)0000-0002-4921-8209
Pages
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Publication Date
2026-06-01
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AI Summary & Key Takeaways

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

We obtained the bias parameter estimation values using the proposed solution method, considering various dependency structures, sample sizes, variance, and number of variables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Multicollinearity is a significant problem in multiple linear regression.
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Key Findings & Data:Different researchers have suggested biased estimators as a possible solution to address the issue of multicollinearity, and an example of a biased estimator is the ridge regression estimator.
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Conclusion & Impact:We obtained the bias parameter estimation values using the proposed solution method, considering various dependency structures, sample sizes, variance, and number of variables.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ridge regression · Biasing parameter · Simulated annealing

Abstract

Multicollinearity is a significant problem in multiple linear regression. Different researchers have suggested biased estimators as a possible solution to address the issue of multicollinearity, and an example of a biased estimator is the ridge regression estimator. Estimating the bias parameter is an essential problem for the ridge regression estimator. This paper presents a new solution method that utilizes simulated annealing optimization to determine the optimal bias parameter as an alternative to the ridge regression bias value proposed by Hoerl and Kennard. We obtained the bias parameter estimation values using the proposed solution method, considering various dependency structures, sample sizes, variance, and number of variables.

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Cite This Article

Kocasoy, G. İ., Ebegil, M., Özdemir, M. (2026). Simulated Annealing Algorithm Based Ridge Estimator. Gazi University Journal of Science, Volume: 39 Issue: 2. https://doi.org/10.35378/gujs.1727816

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