Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 40 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

REAMP: a stochastic resonance approach for multi-change point detection in high-dimensional data

Related

  1. Phi‑Modulated E8 Root‑Vector Stochastic Resonance for Ultra‑Sensitive Signal Detection — E8 Intelligence Research
    Andrew Stewart Caldin
    ZenodoPreprintCC BY 4.0Zenodo
  2. Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection
    Serhii Zabolotnii
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  3. Nonparametric Change-Point Detection and Inference for High-Dimensional Distributions
    Ping Zhao, Long Feng
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  4. Adaptable High-Dimensional Change Point Detection via Ridge Regularization
    Haoran Li, Haotian Xu
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  5. Multiple Change Point Detection in the Mean and Covariance of High-Dimensional Matrix Time Series
    Zetai Cen, Haeran Cho
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
BibTeX
@article{shi2026reamp,
  title = {{REAMP: a stochastic resonance approach for multi-change point detection in high-dimensional data}},
  author = {Xiaoping Shi and Baisuo Jin and Xianhui Liu and Qiong Li},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-74928-z},
  url = {https://www.nature.com/articles/s41598-026-74928-z},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - REAMP: a stochastic resonance approach for multi-change point detection in high-dimensional data
AU  - Xiaoping Shi
AU  - Baisuo Jin
AU  - Xianhui Liu
AU  - Qiong Li
PY  - 2026
DA  - 2026/10/14
JO  - Scientific Reports
DO  - 10.1038/s41598-026-74928-z
UR  - https://www.nature.com/articles/s41598-026-74928-z
SN  - 2045-2322
ER  -