# Multi-sequence segmentation via score and higher-criticism tests

@article{Chan2017MultisequenceSV, title={Multi-sequence segmentation via score and higher-criticism tests}, author={Hock Peng Chan and Hao Chen}, journal={arXiv: Statistics Theory}, year={2017} }

We propose local segmentation of multiple sequences sharing a common time- or location-index, building upon the single sequence local segmentation methods of Niu and Zhang (2012) and Fang, Li and Siegmund (2016). We also propose reverse segmentation of multiple sequences that is new even in the single sequence context. We show that local segmentation estimates change-points consistently for both single and multiple sequences, and that both methods proposed here detect signals well, with the…

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## 11 Citations

Discussion of ‘Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection’

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The theoretical guarantee provided by the WBS2.SDLL proposed in Fryzlewicz (2020) and an alternative, MOSUM-based candidate generation method for the SDLL are discussed.

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Two-stage data segmentation permitting multiscale change points, heavy tails and dependence

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A localised application of Schwarz information criterion is proposed which, as a generic methodology, is applicable with any multiscale candidate generating procedure fulfilling mild assumptions, and attains minimax optimality in terms of detection lower bound and localisation for i.i.d. sub-Gaussian errors.

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The theoretical consistency of the proposed localised pruning method is established and it is shown that it achieves minimax optimality in change point localisation in combination with a multiscale extension of the moving sum-based procedure when there are a finite number of change points.

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- Mathematics, Computer ScienceElectronic Journal of Statistics
- 2019

The main finding is the adaptation over such function classes for a universal thresholding, which includes bounded variation functions, and (piecewise) H\"{o}lder functions of smoothness order $ 0 < \alpha \le1$ as special cases.

Seeded Binary Segmentation: A general methodology for fast and optimal change point detection

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This work shows that seeded binary segmentation leads to a near-linear time approach (i.e. linear up to a logarithmic factor) independent of the underlying number of change points, and demonstrates the methodology for high-dimensional settings with an inverse covariance change point detection problem.

Submitted to the Annals of Statistics SEGMENTATION AND ESTIMATION OF CHANGE-POINT MODELS : FALSE POSITIVE CONTROL AND CONFIDENCE REGIONS By

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- 2019

To segment a sequence of independent random variables at an unknown number of change-points, we introduce new procedures that are based on thresholding the likelihood ratio statistic, and give…

Moving Sum Data Segmentation for Stochastic Processes Based on Invariance

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The asymptotic behaviour of the corresponding change point estimators, show consistency and derive the corresponding localisation rates which are minimax optimal in a variety of situations including an unbounded number of changes in Wiener processes with drift are studied.

ST ] 2 3 M ar 2 01 8 SEGMENTATION AND ESTIMATION OF CHANGE-POINT MODELS

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- 2018

To segment a sequence of independent random variables at an unknown number of change-points, we introduce new procedures that are based on thresholding the likelihood ratio statistic. We also study…

Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries

- Computer ScienceArXiv
- 2020

It is demonstrated empirically that the optimistic search methods lead to competitive estimation performance while heavily reducing run-time, and asymptotic minimax optimality for single and multiple change point scenarios is proved.

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