New Perspective on Change-Point Analysis and Statistical Inference
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 195
Special Issue Editors
Interests: change point analysis; sequential data analysis; statistical inferences; high-dimensional data analysis; confidence distributions
Interests: statistics
Interests: change point analysis; nonparametric inference; time series models; empirical likelihood
Interests: applied probability and statistics; multivariate statistical analysis; stochastic frontier model; effect size estimation and application; a priori procedure for estimating the parameters; applied mathematical modeling
Special Issue Information
Dear Colleagues,
Change-points are abrupt structural shifts in distributions, parameters, or dependence that arise across modern data, including high-frequency time series, spatio-temporal fields, networks, functional and event data, and streaming systems. Classical segmentation and monitoring struggle with scale, heterogeneity, and complex dependence, which motivates new perspectives that join principled detection with valid statistical inference. This Special Issue welcomes contributions that advance methodology, theory, computation, and applications in change-point analysis at the interface with inference. We especially encourage work that (i) delivers scalable, robust, and distribution-agnostic procedures for multiple and sequential detection; (ii) links detection and localization to uncertainty quantification, selective or post-selection or conditional inference, and confidence sets for change locations; (iii) addresses high-dimensional and nonparametric settings, heavy tails, long-range dependence, and adversarial or contaminated data; and (iv) demonstrates impact through reproducible benchmarks, open-source software, and applications in biomedicine and EHR, genomics and neuroscience, industrial process control, economics and finance, climate and remote sensing, cybersecurity and anomaly detection, and online experimentation. Both original research articles and reviews are welcome. We look forward to your contributions to a collection that sets practical and rigorous directions for future work in change-point methodology and statistical inference.
Dr. Suthakaran Ratnasingam
Dr. Chao Gu
Dr. Ramadha Piyadi Gamage
Prof. Dr. Tonghui Wang
Guest Editors
Manuscript Submission Information
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Keywords
- change-point detection
- multiple change-points
- sequential and online monitoring
- high-dimensional inference
- nonparametric methods
- robust or distribution-free procedures
- selective or post-selection inference
- networks and functional data
- uncertainty quantification
- scalable algorithms
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