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Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2026 / Volume: 42 Issue: 1

An amalgamation of crisp and fuzzy quantile regression model

Saima Mustafa(Department of Mathematics and Statistics, PMAS-Arid Agriculture University Rawalpindi, 46300, Pakistan)0000-0002-0584-1445
Hina Basharat(Department of Mathematics and Statistics, PMAS-Arid Agriculture University Rawalpindi, 46300, Pakistan)0000-0002-8120-9089
Ali Akgul(Department of Mathematics, Faculty of Art and Science, Siirt University, Siirt, 56100, Türkiye; Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu, 602105, India)0000-0001-9832-1424
Mohsin Shahzad(Department of Mathematics and Statistics, PMAS-Arid Agriculture University Rawalpindi, 46300, Pakistan)0009-0009-0710-4500
Abdelhamied Farrag Sayed(Department of Mathematics, Al-Lith University College, Umm Al Qura University, P. O Box 112, Al-Lith 21961, Makkah Al Mukaramah, 21955, Kingdom of Saudi Arabia)0000-0002-8067-0631
Pages
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Publication Date
2024-02-27
DOI
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AI Summary & Key Takeaways

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

Empirical study shows that the proposed technique works best in every situation where the fuzzy regression fails and also provide the results in depth.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Fuzzy set theory is the most powerful tool to describe the process of uncertainty which exist in real world and fuzzy regression is an important research topic which can be used for predic-tion by establishing the functional relationship between fuzzy variables.
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Key Findings & Data:Quantile regression is also a significant statistical method for estimating and drawing inferences about conditional quantile functions.
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Conclusion & Impact:Empirical study shows that the proposed technique works best in every situation where the fuzzy regression fails and also provide the results in depth.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Fuzzy Regression · Quantile Regression · Fuzzy Data

Abstract

Fuzzy set theory is the most powerful tool to describe the process of uncertainty which exist in real world and fuzzy regression is an important research topic which can be used for predic-tion by establishing the functional relationship between fuzzy variables. Quantile regression is also a significant statistical method for estimating and drawing inferences about conditional quantile functions. This study introduced the idea of quantile regression with respect to fuzzy. The ordinary fuzzy regression is based on least square method but here we have introduced the idea of weighted least absolute deviation method in fuzzy regression. We have considered two different cases for the illustration of our proposed technique, firstly when the input and output are taken as fuzzy and secondly, the input and output are taken as fuzzy but the param-eters are crisp. The algorithm for each case is based on linear programming problem (LPP). The LPP is constructed for individual case and solved it by the method of Simplex procedure. The proposed work is then compared with the conventional fuzzy regression by using AIC criterion. Empirical study shows that the proposed technique works best in every situation where the fuzzy regression fails and also provide the results in depth.

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Cite This Article

Mustafa, S., Basharat, H., Akgul, A., Shahzad, M., Sayed, A. F. (2024). An amalgamation of crisp and fuzzy quantile regression model. Sigma Mühendislik ve Fen Bilimleri Dergisi, Volume: 42 Issue: 1. https://ajindex.com/articles/1344555

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