AM  Vol.2 No.10 , October 2011
A Note on Change Point Detection Using Weighted Least Square
Abstract: This paper is concerned with the application of weighted least square method in change point analysis. Testing shift in the mean normal observations with time varying variances as well as of a GARCH time series are considered. For both cases, the weighted estimators are given and their asymptotic behaviors are studied. It is also described that how the resampling methods like Monte Carlo and bootstrap may be applied to compute the finite sample behavior of estimators.
Cite this paper: nullR. Habibi, "A Note on Change Point Detection Using Weighted Least Square," Applied Mathematics, Vol. 2 No. 10, 2011, pp. 1309-1312. doi: 10.4236/am.2011.210182.

[1]   M. Csorgo and L. Horvath, “Limit Theorems in Phange-Point Analysis,” Wiley & Sons, New York, 1997.

[2]   J. Chen and A. K. Gupta, “Parametric Statis-tical Change Point Analysis,” Birkh?user, Basel, 2000.

[3]   A. Khodadadi and M. Asgharian, “Change Point Problem and Regression: An Annotated Bibliogra-phy,” Technical Report, McGill University, Montreal, 2004.

[4]   J. Bai, “Least Squares Estimation of a Shift in Linear Processes,” Journal of Time Series Analysis, Vol. 15, No. 5, 1994, pp. 453-472. doi:10.1111/j.1467-9892.1994.tb00204.x

[5]   S. Lee, Y. Tokutsu and K. Maekawa, “The CUSUM Test for Para-meter Change in Regression Models with ARCH Errors,” Journal of Japan Statistical Society, Vol. 3, 2004, pp. 173-186.

[6]   S. Chatterjee and A. Bose, “Generalized Bootstrap for Estimating Equations,” Annals of Statistics, Vol. 33, No. 1, 2005, pp. 414-436. doi:10.1214/009053604000000904

[7]   R. Habibi, “Change Point Detection Using Weighted Least Square,” Technical Report, Central Bank of Iran, 2010.