A new algorithm in detecting changepoint in linear regression models

The changepoint problem in a two-phase simple linear regression model has received increasing attentions. A changepoint in copy number variations in bioinformatics and medical informatics, pattern recognitions, data mining, and many other applications, refers to a time point at which a structural pattern change occurs during a long-term experimentation process. Given a series of observations, the problem is to detect a putative changepoint in the series. Computations in detecting a changepoint is typically time-consuming and inefficient. Recently Liu and Qian (2010) proposed an interesting and computationally easy algorithm via empirical likelihood methods. In this article, a new algorithm is proposed to improve the detecting power. The new algorithm is computationally as easy as Liu and Qian´s algorithm. Simulation results show that the new algorithm greatly improves the detecting powers and hit rates over Liu and Qian´s algorithm.