A comparison of estimators for regression models with change points |
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Authors: | Email author" target="_blank">Cathy?W?S?ChenEmail author Jennifer?S?K?Chan Richard?Gerlach William?Y?L?Hsieh |
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Institution: | 1.Graduate Institute of Statistics & Actuarial Science,Feng Chia University,Taichung,Taiwan;2.School of Mathematics and Statistics,University of Sydney,Sydney,Australia;3.Faculty of Economics and Business,University of Sydney,Sydney,Australia |
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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. |
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Keywords: | |
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