A nonparametric procedure for changepoint detection in linear regression. Issue 8 (18th April 2021)

• Record Type: Journal Article
• Authors: Sun, Jing; Sakate, Deepak; Mathur, Sunil
• Is Part Of: Communications in statistics. Volume 50:Issue 8(2021)
• Page Start: 1925
• Page End: 1935
• Publication Date: 2021-04-18
• Subjects: Least squares estimator -- rank regression -- two phase linear regression -- F test -- non normal error; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1657453
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: Changepoint detection in linear regression has many applications in climatology, bioinformatics, finance, oceanography and medical imaging. In this article, we propose a procedure to detect changepoint in linear regression based on a nonparametric method. The proposed procedure performs well for non normal error distribution and does not require the assumption of normal distribution. A simulation study is conducted to compare the performance of the proposed procedure with the existing procedure, considering the error distribution as Laplace, Student's t, and mixture of normal distributions. The simulation study indicates that the proposed procedure outperforms its competitor. A real-life example is used to illustrate the working procedure.