Presenter Information

Jing SunFollow

Presentation Title

A Non Parametric Procedure for Change Point Detection in Linear Regression

Location

Nessmith-Lane Atrium

Session Format

Poster Presentation

Research Area Topic:

Public Health & Well Being - Quality Improvement/Recession Impact

Abstract

Change point detection in linear regression has many applications in climatology, bioinformatics, finance, oceanography and medical imaging. There are some parametric methods to locate change point in linear regression in the literature. Lund and Reeves (2002) proposed a procedure based on F-test for detecting change points in two phase linear regression model assuming normal distribution for error. The F-test is based on the least squares estimator which is optimal under the normality of errors. In this article, we develop a procedure to detect change point in linear regression based on a non- parametric test in McKean and Hettmansperger (1976). The proposed procedure is intended to perform well for non-normal error distribution and is non parametric in nature. A simulation study to compare the performance of the proposed procedure with the procedure in Lund and Reeves (2002) is conducted for Laplace, Student‰Ûªs t, Slash and Cauchy distributions for error. The results of the simulation study reveal that the proposed procedure outperforms its competitor while the change point detection procedure based on F test (Lund and Reeves, 2002) is found to be suboptimal when the error distribution is non-normal.

Presentation Type and Release Option

Presentation (Open Access)

Start Date

4-16-2016 10:45 AM

End Date

4-16-2016 12:00 PM

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Apr 16th, 10:45 AM Apr 16th, 12:00 PM

A Non Parametric Procedure for Change Point Detection in Linear Regression

Nessmith-Lane Atrium

Change point detection in linear regression has many applications in climatology, bioinformatics, finance, oceanography and medical imaging. There are some parametric methods to locate change point in linear regression in the literature. Lund and Reeves (2002) proposed a procedure based on F-test for detecting change points in two phase linear regression model assuming normal distribution for error. The F-test is based on the least squares estimator which is optimal under the normality of errors. In this article, we develop a procedure to detect change point in linear regression based on a non- parametric test in McKean and Hettmansperger (1976). The proposed procedure is intended to perform well for non-normal error distribution and is non parametric in nature. A simulation study to compare the performance of the proposed procedure with the procedure in Lund and Reeves (2002) is conducted for Laplace, Student‰Ûªs t, Slash and Cauchy distributions for error. The results of the simulation study reveal that the proposed procedure outperforms its competitor while the change point detection procedure based on F test (Lund and Reeves, 2002) is found to be suboptimal when the error distribution is non-normal.