Robust algorithms for multiphase regression models

Kang Ping Lu, Shao Tung Chang

研究成果: 雜誌貢獻文章

摘要

This paper proposes a robust procedure for solving multiphase regression problems that is efficient enough to deal with data contaminated by atypical observations due to measurement errors or those drawn from heavy-tailed distributions. Incorporating the expectation and maximization algorithm with the M-estimation technique, we simultaneously derive robust estimates of the change-points and regression parameters, yet as the proposed method is still not resistant to high leverage outliers we further suggest a modified version by first moderately trimming those outliers and then implementing the new procedure for the trimmed data. This study sets up two robust algorithms using the Huber loss function and Tukey's biweight function to respectively replace the least squares criterion in the normality-based expectation and maximization algorithm, illustrating the effectiveness and superiority of the proposed algorithms through extensive simulations and sensitivity analyses. Experimental results show the ability of the proposed method to withstand outliers and heavy-tailed distributions. Moreover, as resistance to high leverage outliers is particularly important due to their devastating effect on fitting a regression model to data, various real-world applications show the practicability of this approach.

原文英語
頁(從 - 到)1643-1661
頁數19
期刊Applied Mathematical Modelling
77
DOIs
出版狀態已發佈 - 2020 一月
對外發佈Yes

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics

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