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Detection performance of likelihood ratio test for change points based on bootstrap for AR 1 models
Date
2016-08-26
Author
Yazıcı, Ceyda
Yozgatlıgil, Ceylan
Batmaz, İnci
Metadata
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he detection of change-points in time series is an important issue especially in economics, finance, meteorology and energy. Change in mean, change in variance or any sudden increase or decrease in the series can cause breakpoints. In AR(1) models, the likelihood ratio test is conducted to test for a single breakpoint. However, if the sample size is small or the location of the breakpoint is close to the end or the beginning of the series, the detection performance becomes worse. In order to increase the correct detection percentage of the likelihood ratio test in these cases, a bootstrap method for dependent data is applied and its performance is investigated when the change is only in the mean under several breakpoint scenarios. The test is applied to simulated data and the results are compared with the results obtained from tests in the literature.
Subject Keywords
Bootstrap
,
Change points
,
Simulation
,
Time series
URI
http://cmstatistics.org/RegistrationsV2/COMPSTAT2016/viewSubmission.php?id=527&token=np263qr78sn50660043or043rr3p63qo
https://hdl.handle.net/11511/78839
Conference Name
The 22nd International Conference on Computational Statistics, ( 23 - 26 Ağustos 2016)
Collections
Department of Statistics, Conference / Seminar
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C. Yazıcı, C. Yozgatlıgil, and İ. Batmaz, “Detection performance of likelihood ratio test for change points based on bootstrap for AR 1 models,” presented at the The 22nd International Conference on Computational Statistics, ( 23 - 26 Ağustos 2016), Oviedo, İspanya, 2016, Accessed: 00, 2021. [Online]. Available: http://cmstatistics.org/RegistrationsV2/COMPSTAT2016/viewSubmission.php?id=527&token=np263qr78sn50660043or043rr3p63qo.
