A multiple window scan statistic for time series models |
| |
Affiliation: | 1. Department of Probability and Statistics, Beijing Institute of Technology, Beijing, 100081, China;2. Department of Statistics, North China University of Technology, Beijing, 100144, China;1. Afdeling Statistiek, Celestijnenlaan 200b - bus 2400, 3001 Leuven, Belgium;2. Faculty of Business and Economics, ORSTAT, KU Leuven, Belgium;1. University of Pittsburgh, United States;2. University of Haifa, Israel |
| |
Abstract: | In this article we extend the results derived for scan statistics in Wang and Glaz (2014) for independent normal observations. We investigate the performance of two approximations for the distribution of fixed window scan statistics for time series models. An R algorithm for computing multivariate normal probabilities established in Genz and Bretz (2009) can be used along with proposed approximations to implement fixed window scan statistics for ARMA models. The accuracy of these approximations is investigated via simulation. Moreover, a multiple window scan statistic is defined for detecting a local change in the mean of a Gaussian white noise component in ARMA models, when the appropriate length of the scanning window is unknown. Based on the numerical results, for power comparisons of the scan statistics, we can conclude that when the window size of a local change is unknown, the multiple window scan statistic outperforms the fixed window scan statistics. |
| |
Keywords: | ARMA models Gaussian white noise Moving sum Multiple window scanning |
本文献已被 ScienceDirect 等数据库收录! |
|