Fuzzy Rank-Based Clustering for Change Point Detection in Regression

, Hazem

doi:10.25673/123539

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 107–114

Fuzzy Rank-Based Clustering for Change Point Detection in Regression

Hazem Abd razaq and Mohammed Jasim Mohammed

📄 Download PDF DOI: 10.25673/123539

Abstract

Detecting change points in regression models is an essential statistical method for analysing phenomena that change abruptly or occur in multiple stages. Determining changes in data helps researchers to analyse variations and improve the accuracy of statistical and predictive models. This analysis becomes more critical when data contains outliers or errors that follow a heavy-tailed distribution, especially as traditional methods become less effective at detecting changes and estimating model coefficients. In this paper, we propose a new robust fuzzy method called FRBCP, which combines the fuzzy cluster algorithm with a rank-based estimator. This combination allows for dealing with fuzzy change points, reducing the influence of outliers, and achieving robustness of the estimate. The proposed method was compared with the fuzzy change point algorithm and Muggeo methods through numerical experiments and real data. The results showed that the proposed methods are satisfactory in detecting change points and estimating model coefficients, and are mainly effective when the data contains outliers or an error-heavy-tailed distribution.

Keywords

Fuzzy Change Points Multistage Regression Rank-Based Estimator Fuzzy Rank-Based.

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