Statistics and Computing

, Volume 21, Issue 3, pp 395–414

A comparison of estimators for regression models with change points

  • Cathy W. S. Chen
  • Jennifer S. K. Chan
  • Richard Gerlach
  • William Y. L. Hsieh
Article

DOI: 10.1007/s11222-010-9177-0

Cite this article as:
Chen, C.W.S., Chan, J.S.K., Gerlach, R. et al. Stat Comput (2011) 21: 395. doi:10.1007/s11222-010-9177-0

Abstract

We consider two problems concerning locating change points in a linear regression model. One involves jump discontinuities (change-point) in a regression model and the other involves regression lines connected at unknown points. We compare four methods for estimating single or multiple change points in a regression model, when both the error variance and regression coefficients change simultaneously at the unknown point(s): Bayesian, Julious, grid search, and the segmented methods. The proposed methods are evaluated via a simulation study and compared via some standard measures of estimation bias and precision. Finally, the methods are illustrated and compared using three real data sets. The simulation and empirical results overall favor both the segmented and Bayesian methods of estimation, which simultaneously estimate the change point and the other model parameters, though only the Bayesian method is able to handle both continuous and dis-continuous change point problems successfully. If it is known that regression lines are continuous then the segmented method ranked first among methods.

Keywords

Change pointJump discontinuitiesMCMCGrid-searchSegmented regression

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Cathy W. S. Chen
    • 1
  • Jennifer S. K. Chan
    • 2
  • Richard Gerlach
    • 3
  • William Y. L. Hsieh
    • 1
  1. 1.Graduate Institute of Statistics & Actuarial ScienceFeng Chia UniversityTaichungTaiwan
  2. 2.School of Mathematics and StatisticsUniversity of SydneySydneyAustralia
  3. 3.Faculty of Economics and BusinessUniversity of SydneySydneyAustralia