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Parallelization of Change Point Detection
The Journal of Physical Chemistry A, 2017The change point detection method ( Watkins , L. P. ; Yang , H. J. Phys. Chem. B 2005 , 109 , 617 ) allows the objective identification and isolation of abrupt changes along a data series. Because this method is grounded in statistical tests, it is particularly powerful for probing complex and noisy signals without artificially imposing a kinetics ...
Nancy Song, Haw Yang
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Change‐point detection in panel data
Journal of Time Series Analysis, 2012We consider N panels and each panel is based on T observations. We are interested to test if the means of the panels remain the same during the observation period against the alternative that the means change at an unknown time. We provide tests which are derived from a likelihood argument and they are based on the adaptation of the CUSUM method to ...
Horváth, Lajos, Hušková, Marie
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Change Point Detection Techniques
2004In this chapter we will discuss change point detection techniques for analyzing population change over time. The linkage between information theory and statistics has been discussed in Chapter 5. In statistics, the problem of change point analysis can be formulated as that of model selection.
Bon K. Sy, Arjun K. Gupta
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Change-Point Detection in Angular Data
Annals of the Institute of Statistical Mathematics, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Grabovsky, Irina, Horváth, Lajos
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NONPARAMETRIC TRUNCATED SEQUENTIAL CHANGE-POINT DETECTION
Statistics & Risk Modeling, 1995Summary: New nonparametric truncated sequential changepoint detection processes are considered via a theorem of \textit{D. A. Darling} and \textit{P. Erdős} [Duke Math. J. 23, 143-155 (1956; Zbl 0070.138)]. Their asymptotic and finite sample properties are examined and compared to existing procedures.
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Hubness Change Point Detection
Proceedings of the AAAI Conference on Artificial IntelligenceThis study proposes a new change detection method that leverages hubness. Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. Hubness is known to degrade the accuracy of methods based on nearest neighbor search.
Ikumi Suzuki, Kazuo Hara, Eiji Murakami
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Greedy Kernel Change-Point Detection
IEEE Transactions on Signal Processing, 2019We consider the problem of detecting abrupt changes in the underlying stochastic structure of multivariate signals. A novel non-parametric and model-free off-line change-point detection method based on a kernel mapping is presented. This approach is sequential and alternates between two steps: a greedy detection to estimate a new breakpoint and a ...
Charles Truong +2 more
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Adaptive Change Point Detection for Respiratory Variables
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005Current alarm strategies for physiological monitoring depend on predetermined thresholds without consideration for the heterogeneity between patients or intraoperative variations. To improve upon this situation, we developed an adaptive change point detection scheme to automatically notify the clinician when a change of clinical significance has ...
Ping, Yang +3 more
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Multiple change‐point detection for regression curves
Canadian Journal of StatisticsAbstractNonparametric estimation of a regression curve becomes crucial when the underlying dependence structure between covariates and responses is not explicit. While existing literature has addressed single change‐point estimation for regression curves, the problem of multiple change points remains unresolved.
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