Robust continuous piecewise linear regression model with multiple change points

Shi, Shurong; Li, Yang; Wan, Chuang

doi:10.1007/s11227-018-2597-x

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…It is best used when there is a breaking point in the dataset or a U shaped relationship (Xu et al, 2016). A single linear model will not provide an adequate description of the relationship (Shi et al, 2020). Often a nonlinear model will be most appropriate in this situation.…”

Section: Piecewise Regression Analysismentioning

confidence: 99%

Exploring the Nature of the Relationship Between Age and Entrepreneurial Intentions

Afzal

2024

Preprint

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<p>This is a study into perceived behavioural control (PBC) and takes a closer look at its interaction with biological age of an entrepreneur. Literature suggests a curvilinear relationship between age and entrepreneurial intentions (EI). The main research question of this study is whether a similar relationship exists between age and PBC, one of the main antecedents of EI. This study explored the relationship between age and PBC while controlling for gender, access to financial, human and cultural capital. The study deployed regression method for data analysis. The finding of the study shows that age has an impact on PBC, it shows a gradual increase among the young participants and a steep decrease for older participants. The finding has practical and academic implications for academia, government and the private sector.</p>

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Section: Piecewise Regression Analysismentioning

confidence: 99%

Exploring the Nature of the Relationship Between Age and Entrepreneurial Intentions

Afzal

2024

Preprint

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“…Particularly, Muggeo [28] further introduced an R package called "segmented" to facilitate the use of the segmented regression method. The SEG method has been widely employed in a great many applications [28][29][30][31][32], especially in several recent analyses of the COVID-19 epidemic curve [33][34][35]. Because of its easiness and high efficiency, the segmented regression method was further combined with rank-based estimation to make the segmented method more resistant against outliers (c.f.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because of its easiness and high efficiency, the segmented regression method was further combined with rank-based estimation to make the segmented method more resistant against outliers (c.f. [7,32]). Many researchers [7,12,32,36] recognized that the SEG method works well when the piecewise model is known to be continuous at change-points.…”

Section: Literature Reviewmentioning

confidence: 99%

An Advanced Segmentation Approach to Piecewise Regression Models

Lu,

Chang

2023

Mathematics

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Two problems concerning detecting change-points in linear regression models are considered. One involves discontinuous jumps in a regression model and the other involves regression lines connected at unknown places. Significant literature has been developed for estimating piecewise regression models because of their broad range of applications. The segmented (SEG) regression method with an R package has been employed by many researchers since it is easy to use, converges fast, and produces sufficient estimates. The SEG method allows for multiple change-points but is restricted to continuous models. Such a restriction really limits the practical applications of SEG when it comes to discontinuous jumps encountered in real change-point problems very often. In this paper, we propose a piecewise regression model, allowing for discontinuous jumps, connected lines, or the occurrences of jumps and connected change-points in a single model. The proposed segmentation approach can derive the estimates of jump points, connected change-points, and regression parameters simultaneously, allowing for multiple change-points. The initializations of the proposed algorithm and the decision on the number of segments are discussed. Experimental results and comparisons demonstrate the effectiveness and superiority of the proposed method. Several real examples from diverse areas illustrate the practicability of the new method.

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“…In the specific case of segmented regression, robust techniques have also been explored. For instance, Zhang and Li (2017) and Shi et al (2020) introduced rank-based estimation methods, Zhou et al (2020) proposed a least trimmed squares estimator, and Bagirov et al (2022) developed an L 1 -estimator approach.…”

Section: Introductionmentioning

confidence: 99%

Robust segmented regression: application to oxygen uptake plateau identification

Sarnaglia,

Fajardo Molinares,

Azevedo

2023

Environ Ecol Stat

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Recently, segmented regression has been utilized as a "working" model for a bootstrap test to detect true oxygen uptake plateau. This approach employs an iterative procedure based on least squares to fit the model. However, it is widely acknowledged that least squares is highly sensitive to outliers, often yielding inefficient estimates. This paper proposes an alternative iterative method by substituting the least squares step with a M-estimator step. Leveraging the robust features of M-estimators, the proposed method is expected to exhibit resilience against outliers. To empirically investigate the performance of the proposed method, a Monte Carlo simulation study is conducted. For comparison purposes, the classical iterative method is also considered. The results indicate that, in uncontaminated scenarios, both methods exhibit similar behavior, with the classical method presenting a slightly superior performance. However, in contaminated cases, the classical method is highly deteriorated, with significant bias and root mean squared error, while the proposed method demonstrates much better performance. Both methods are employed to fit the "working" 3-segment regression model and perform a plateau test on apparently contaminated oxygen uptake data. The results reveal that, due to the presence of outliers, the classical method produces inflated estimates for the error variance and larger standard errors for all parameters, in comparison to the proposed method. Moreover, the plateau test conducted using the classical (robust) method leads to the rejection (confirmation) of a plateau of oxygen uptake for the same data. Overall, these findings highlight the superiority of the proposed method in handling outliers.

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Robust continuous piecewise linear regression model with multiple change points

Cited by 10 publications

References 18 publications

Exploring the Nature of the Relationship Between Age and Entrepreneurial Intentions

Exploring the Nature of the Relationship Between Age and Entrepreneurial Intentions

An Advanced Segmentation Approach to Piecewise Regression Models

Robust segmented regression: application to oxygen uptake plateau identification

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