Off-Line Detection of Multiple Change Points by the Filtered Derivative withp-Value Method

DOI10.1080/07474946.2011.563710zbMath1215.62076arXiv1003.4148OpenAlexW1977661272MaRDI QIDQ3006704

Mehdi Fhima, Arnaud Guillin, Pierre R. Bertrand

Publication date: 20 June 2011

Published in: Sequential Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1003.4148

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Sequential statistical analysis (62L10)

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Confidence intervals for annual wind power production ⋮ Classifying heartrate by change detection and wavelet methods for emergency physicians ⋮ High-dimensional change-point estimation: combining filtering with convex optimization ⋮ The shark fin function: asymptotic behavior of the filtered derivative for point processes in case of change points ⋮ Optimal change point detection in Gaussian processes ⋮ Temporal dynamics of inter-limb coordination in ice climbing revealed through change-point analysis of the geodesic mean of circular data ⋮ A multiple filter test for the detection of rate changes in renewal processes with varying variance ⋮ The multiple filter test for change point detection in time series ⋮ A multi-scale approach for testing and detecting peaks in time series

Cites Work

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• Detecting multiple change-points in the mean of Gaussian process by model selection
• Algebraic change-point detection
• Dynamic detection of change points in long time series
• Monitoring parameter change in AR\((p)\) time series models
• Exploring the state sequence space for hidden Markov and semi-Markov chains
• On extreme value asymptotics for increments of renewal processes
• Minimal penalties for Gaussian model selection
• Detection of multiple change-points in multivariate time series
• Least‐squares Estimation of an Unknown Number of Shifts in a Time Series
• Change-Point Analysis Based on Empirical Characteristic Functions of Ranks
• Bootstrapping Sequential Change-Point Tests
• An approximation of partial sums of independent RV'-s, and the sample DF. I
• A local method for estimating change points: the “Hat-function”
• Estimating and Testing Linear Models with Multiple Structural Changes
• Asymptotic Properties of Gaussian Processes

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