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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The anomaly scores for each feature are individually used as values for the cusum algorithm to detect disturbances.
“The anomaly scores for each feature in the observation are then individually used as values for the cusum algorithm (Page, 1954) to detect any disturbances.”
From Novelty Detection in Reinforcement Learning with World Models (2023) · cites this paper as Page, 1954
Cited by
Sorted by citations
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<b>changepoint</b> : An <i>R</i> Package for Changepoint Analysis
Killick, R., & Eckley, I. A. (2014) · Journal of Statistical Software · doi:10.18637/jss.v058.i03 · Cited by 1,330 · see citing works
Ensemble learning for data stream analysis: A survey
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Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423 · see citing works
A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease
Farrington, C. P., Andrews, N. J., Beale, A. D., & Catchpole, M. A. (1996) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983331 · Cited by 417 · see citing works
Objective motor response onset detection in surface myoelectric signals
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Timsit, E., Assié, S., Quiniou, R., Seegers, H., & Bareille, N. (2011) · The Veterinary Journal · doi:10.1016/j.tvjl.2010.09.012 · Cited by 120 · see citing works
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Çubukçu, H. C. (2021) · Turkish Journal of Biochemistry · doi:10.1515/tjb-2021-0199 · Cited by 13 · see citing works
Novelty Detection in Reinforcement Learning with World Models
Zollicoffer, G., Eaton, K., Balloch, J., Kim, J., Zhou, W., Wright, R., & Riedl, M. O. (2023) · arXiv · doi:10.48550/arxiv.2310.08731
- Authors
- PAGE, E. S.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- PAGE, E. S.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1954
- Volume
- 41
- Issue
- 1-2
- Pages
- 100-115
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2051903196
- Cited by
- 5,739
- References
- 0
- Citations by year
- 2026: 410 · 2025: 316 · 2024: 321 · 2023: 294 · 2022: 263 · 2021: 368 · 2020: 339 · 2019: 272 · 2018: 290 · 2017: 240 · 2016: 234 · 2015: 210 · 2014: 211 · 2013: 212 · 2012: 188
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrika (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- acceptance sampling, statistical quality control, sampling plans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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