ArticleMetrika2022
Detecting multiple generalized change-points by isolating single ones.
Article in Metrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Frequency Band Analysis of Multiple Stationary Time Series.Statistics in medicine · 2026Article
- From microstates to macroscales: A Critical Review of Maximum Entropy Modeling and Energy Landscape Analysis in functional MRI.Research square · 2025Article
- Factorized binary search: Change point detection in the network structure of multivariate high-dimensional time series.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Objective Bayesian trend filtering via adaptive piecewise polynomial regression.Journal of applied statistics · 2025Article
- A Selective Review on Information Criteria in Multiple Change Point Detection.Entropy (Basel, Switzerland) · 2024Review
- Predicting alfalfa leaf area index by non-linear models and deep learning models.Frontiers in plant science · 2024Article
- A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles.Scientific reports · 2023Article
- The impact of government actions and risk perception on the promotion of self-protective behaviors during the COVID-19 pandemic.PloS one · 2023Article
- Detection and estimation of multiple transient changes.Journal of applied statistics · 2023Article
- Testing for a Change in Mean After Changepoint Detection.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus.Scientific reports · 2021Article
- Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection-rejoinder.Journal of the Korean Statistical Society · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID's accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s00184-021-00821-6.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.