A segmentation approach to regression problems with switching-points

Shao Tung Chang; Kang Ping Lu

doi:10.1109/ICTC58733.2023.10393621

A segmentation approach to regression problems with switching-points

Shao Tung Chang^*, Kang Ping Lu^*

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Regression problems with switching-points arise in many fields and has been recognized as a challenging issue for modern, big data applications. Many approaches to estimating switching regression models can only deal with continuous switching-point problem successfully. However, regression problems with jump discontinuities are often encountered in reality such as those in econometrics and engineering. This article presents a segmentation method for switching regression estimations, allowing for detecting both continuous and discontinuous switching-points. We consider using Taylor's expansion with an adjustment constant to derive the estimates of switching-points and regression parameters simultaneously. The proposed method can detect both jump-points and continuous switching-points. The proposed method is evaluated via experiments with numerical examples. The simulation results show the proposed method work well for both continuous and dis-continuous models and produce rather accurate estimates.

Original language	English
Title of host publication	ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Exploring the Frontiers of ICT Innovation
Publisher	IEEE Computer Society
Pages	436-439
Number of pages	4
ISBN (Electronic)	9798350313277
DOIs	https://doi.org/10.1109/ICTC58733.2023.10393621
Publication status	Published - 2023
Event	14th International Conference on Information and Communication Technology Convergence, ICTC 2023 - Jeju Island, Korea, Republic of Duration: 2023 Oct 11 → 2023 Oct 13

Publication series

Name	International Conference on ICT Convergence
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	14th International Conference on Information and Communication Technology Convergence, ICTC 2023
Country/Territory	Korea, Republic of
City	Jeju Island
Period	2023/10/11 → 2023/10/13

Keywords

Taylor expansion
jump
switching regression
switching-point

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC58733.2023.10393621

Cite this

Chang, S. T., & Lu, K. P. (2023). A segmentation approach to regression problems with switching-points. In ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation (pp. 436-439). (International Conference on ICT Convergence). IEEE Computer Society. https://doi.org/10.1109/ICTC58733.2023.10393621

A segmentation approach to regression problems with switching-points. / Chang, Shao Tung; Lu, Kang Ping.
ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. IEEE Computer Society, 2023. p. 436-439 (International Conference on ICT Convergence).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chang, ST & Lu, KP 2023, A segmentation approach to regression problems with switching-points. in ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. International Conference on ICT Convergence, IEEE Computer Society, pp. 436-439, 14th International Conference on Information and Communication Technology Convergence, ICTC 2023, Jeju Island, Korea, Republic of, 2023/10/11. https://doi.org/10.1109/ICTC58733.2023.10393621

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abstract = "Regression problems with switching-points arise in many fields and has been recognized as a challenging issue for modern, big data applications. Many approaches to estimating switching regression models can only deal with continuous switching-point problem successfully. However, regression problems with jump discontinuities are often encountered in reality such as those in econometrics and engineering. This article presents a segmentation method for switching regression estimations, allowing for detecting both continuous and discontinuous switching-points. We consider using Taylor's expansion with an adjustment constant to derive the estimates of switching-points and regression parameters simultaneously. The proposed method can detect both jump-points and continuous switching-points. The proposed method is evaluated via experiments with numerical examples. The simulation results show the proposed method work well for both continuous and dis-continuous models and produce rather accurate estimates.",

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A segmentation approach to regression problems with switching-points

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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