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REVIEW 3 major objections 5 minor 234 references

focus and focus-cpt: Fast Online Changepoint Detection in R and Python

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Exact GLR changepoint detection is now fast enough to run online

desk verdict A solid, useful software paper for the focus changepoint family; the abstract's unconditional 'exact, without approximations' claim is too broad because the high-dimensional projection mode is approximate. read the letter →

arxiv 2607.19961 v1 pith:MTVFGRNR submitted 2026-07-22 stat.ME

Gaetano Romano , Kes Ward , Yuntang Fan , Guillem Rigaill , Vincent Runge , Idris A. Eckley , Paul Fearnhead This is my paper · ORCID
classification stat.ME MSC 62L1062M10
keywords changepointdetectiononlinealgorithmsGeneralisedLikelihoodRatioconvexhullpruningexponentialfamilyRpackagePythonautoregressive
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper presents focus and focus-cpt, R and Python packages that implement the focus family of online changepoint detection algorithms. The central claim is that the Generalised Likelihood Ratio statistic for a single changepoint can be computed exactly, with no approximation, while the per-iteration cost grows only logarithmically with the length of the observed sequence (log(n)^d for d-dimensional data). This is achieved by pruning the set of candidate changepoint locations to the vertices of a convex hull built from the data, because any candidate lying inside the hull can never be the maximizer. If correct, this means practitioners can run exact likelihood-ratio changepoint detection in real time on streams of counts, means, variances, and other exponential-family data, rather than resorting to approximate or costly methods.

What carries the argument

The key object is the geometric map from changepoint candidates to points P(τ) = (τ, cumulative sufficient statistics) in R^{d+1}, together with the convex hull of those points. The log-likelihood of a change at τ is the scalar product of P(τ) with a model-dependent vector ψ(θ0, θ1), so the argmax over τ is exactly the support point of the hull in the direction ψ. Pruning candidates that lie in the interior of the hull removes all points that cannot win, and the number of hull vertices bounds the per-iteration cost. This geometry is what carries the exactness and the logarithmic complexity claim.

What would settle it

Run the univariate detector on a deterministically convex sequence (e.g., y_t = exp(t)) and count the number of active candidates after each update; if the count grows linearly or per-step wall-clock time grows linearly, the log-cost claim is false for that stream. Separately, compare the full-hull and projection-based statistics on a high-dimensional stream with a change confined to dimensions not covered by the chosen projections; a large discrepancy in the statistic or a missed detection would show the approximation sacrifices exactness.

Watch

Extended reading notes

Core claim

The paper establishes that the GLR statistic for a single changepoint can be maintained exactly in an online fashion by exploiting a convex-hull geometry: each candidate changepoint τ is mapped to a point P(τ) = (τ, Σ_{t≤τ} T(y_t)) in dimension d+1, and the log-likelihood difference for a change at τ is a scalar product of P(τ) with a model-dependent vector. Proposition 1 shows that if P(τ′) lies in the interior of the convex hull of all candidate points, then τ′ can never be the maximiser for any pre- and post-change parameters and can be permanently discarded. Hence the active candidate set is contained in the hull vertices, whose expected size grows only as O(log(n)^d). The package implem

Load-bearing premise

The claimed logarithmic per-iteration cost rests on the expected number of convex-hull vertices growing only as log(n)^d, and on the pruning rule correctly discarding every candidate whose point lies in the interior of the hull; if a stream produces a hull with linearly many vertices, or if the projection shortcut in high dimensions drops a vertex that carries the true change, the speed or exactness is lost.

Editorial extensions

If this is right

  • Real-time monitoring of counts, means, variances, and other exponential-family streams can now use exact GLR statistics at each time step instead of approximations, at logarithmic per-observation cost.
  • Offline threshold calibration becomes practical: focus_offline computes full statistic traces in one pass, enabling Monte Carlo-based false-positive-rate thresholds without rerunning the algorithm for every threshold.
  • Because R and Python share the same C++ backend, results are identical across interfaces, which reduces replication risk and makes the package a reliable common tool for practitioners.
  • For high-dimensional streams, the projection-based hull approximation offers a large speedup at the cost of a small difference in the statistic, as illustrated by the paper's comparison on six-dimensional data.
  • The AR(p) detector accounts for the transient residual behaviour after a change, preserving detection power near the end of the stream where naive pre-whitening would lose it.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The convex-hull pruning insight is not limited to GLR: any sequential test whose statistic is a maximum of linear functions over candidates could reuse the same hull maintenance to update its argmax exactly in sublinear time.
  • The projection-based approximation for high dimensions may miss changes whose signal is concentrated entirely in dimensions not jointly projected; the exposed dim_indexes argument turns this into a modelling choice, but the paper does not quantify worst-case power loss.
  • The lambda weighting mechanism generalises the detector to covariate-scaled rates (e.g., Poisson data with time-varying background), which suggests the same geometric pruning could support non-stationary exposure models without algorithmic change.
  • If a stream's convex hull grows linearly rather than logarithmically, the speed guarantee would degrade gracefully but not hold; a window-limited fallback or a size cap on the candidate set would be a natural robustness extension.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper describes an R and Python package (focus / focus-cpt) implementing the focus family of online changepoint detectors. The core algorithmic idea, imported from previous work by the same group, is to reduce the set of candidate changepoints to geometric extreme points of a data-dependent convex hull, so that the generalized likelihood ratio statistic can be evaluated exactly at a claimed per-iteration cost of roughly log(n)^d for a d-dimensional stream. The package covers one-parameter exponential family models (Gaussian, Poisson, Binomial, Exponential, Gamma), a nonparametric eCDF-based detector, and a detector for autoregressive data, and it provides both online and offline interfaces backed by a shared C++ implementation. The manuscript gives an algorithmic overview, a detailed interface description, and several simulated and real-data examples. The central advertised contribution is that the implementation delivers exact GLR statistics without approximations while remaining fast enough for real-time use.

Significance. If the claims are properly qualified, this is a useful software contribution: it unifies several methodological papers into one maintained package with R and Python interfaces, a shared C++ backend, and reproducible example code on GitHub. The package is available on CRAN and PyPI, which is a concrete strength. However, the headline exactness claim is currently overstated: the body of the paper explicitly introduces an approximate high-dimensional mode that does not compute the full exact GLR statistic. The complexity guarantee is also stated inconsistently and is inherited from an expected-size result rather than established here. These issues are fixable, but they affect the central claims of the paper and should be addressed before publication.

major comments (3)
  1. [Abstract; Section 2.3; Section 4.2] The abstract and Section 1 state that focus computes the GLR statistic 'exactly, without introducing approximations' at O(log(n)^d) cost. Section 2.3, however, introduces for d>5 a projection-based mode: data are projected onto overlapping subsets of d-tilde <= 5 coordinates, hulls are computed in each projection, and the union of the lower-dimensional hull vertices is used as the candidate set. A vertex of the full (d+1)-dimensional hull whose supporting hyperplane uses all coordinates need not be a vertex of any projection, so this candidate set can miss the GLR maximizer. Section 4.2 confirms the issue: all.equal reports a mean relative difference of 0.003782673 between the full-hull and projection-based statistics. Exactness therefore holds only for the full-hull mode; the abstract's unconditional 'without introducing approximations' claim is not supported as written and should be qu
  2. [Section 2.3; Section 1] The paper advertises two incompatible complexity statements. The abstract and Section 1 say the per-iteration cost is approximately log(n)^d, while Section 2.3 says the univariate pruning is amortised O(1); for d=1 these differ by a factor of log n. More importantly, the O(log(n)^d) growth is cited from Corollary 1 of Pishchagina et al. (2025) as an expected hull-size result, but the present paper gives no statement of the conditions under which this expectation holds. Because real-time performance is central to the paper's motivation, either reproduce the relevant bound with its assumptions or clearly state that the efficiency guarantee is inherited and is expected/average-case rather than worst-case.
  3. [Section 2.2, Proposition 1] Proposition 1 prunes only points in the interior of the convex hull, yet the text immediately concludes that D_n is contained in the set of hull vertices. This inference is not immediate: a point on a facet of the hull but not a vertex is not interior, and it can be an argmax of <P(tau), psi> only when the same maximum value is also attained by a hull vertex. To support the stated exactness for general exponential-family models, the paper should either prove that omitting boundary non-vertices cannot change the value of max_tau q_tau, or specify that the algorithm retains such points or resolves ties in a way that preserves the maximum. As written, the proposition alone does not establish the claimed reduction to vertices.
minor comments (5)
  1. [Section 1] Typo: 'unvariate' should be 'univariate'.
  2. [Section 4.1] Only 500 Monte Carlo replications are used to estimate a 99th percentile threshold. This gives a noisy estimate; please report a standard error or use more replications.
  3. [Section 4.2] The text says the projection-based approximation produces 'extremely similar results' despite a reported mean relative difference of 0.0038. Please quantify this more carefully, e.g., with the maximum relative difference and the effect on detection times, since the difference is exactly the kind of approximation error the abstract denies.
  4. [Figure 6] The x-axis of Figure 6 appears to be game index, but the text refers to '640 games' and later to 1000; please clarify the sample size and axis labeling.
  5. [Appendix A] The Python and R examples intentionally produce different stopping times due to different RNGs. This is fine, but the discrepancy may confuse readers; consider a sentence noting that the algorithms are identical and the difference is purely the random seed/stream.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation is either self-contained or rests on independently published geometry results, and the projection-mode caveat is an accuracy limitation rather than a circular step.

full rationale

I walked the claimed derivation chain. The paper's core contribution is an implementation; it does not attempt a from-scratch proof of the focus/GLR geometry. Section 2.1 defines the GLR statistic by standard likelihood maximization. Proposition 1 then gives a self-contained convex-hull pruning proof: any interior point P(tau') is dominated for every (theta0, theta1) by a convex combination of hull points. The logarithmic per-iteration cost is inherited from Corollary 1 of Pishchagina et al. (2025), an externally published theoretical bound. Citing that bound, even with overlapping authorship, is legitimate evidence rather than circular reasoning, because the bound is not assumed true by fitting it here and is not equivalent to the paper's own outputs. No model parameter is fitted to a subset of the target data and then renamed a prediction; the Monte Carlo thresholds in Section 4.1 are calibration/operating parameters, not predicted GLR values. The only tension is that the abstract states that the algorithms compute GLR 'exactly ... without introducing approximations,' while Section 2.3 explicitly describes an 'approximate algorithm' for d>5 using projections and Section 4.2 reports a mean relative difference of 0.003782673 between full-hull and projection-based statistics. This is an internal consistency/scope-of-claim concern, not a circularity: the exact claim is restricted to the full-hull mode, and the high-dimensional mode is openly labeled approximate. I found no self-definitional reduction, no fitted-input-called-prediction, no uniqueness theorem imported to force the answer, and no ansatz smuggled in by self-citation. Therefore the paper receives a circularity score of 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim leans on standard convex geometry and on results inherited from earlier focus papers; the only hand-tuned numbers are user-facing defaults and example thresholds. No new entities are introduced.

free parameters (4)
  • pruning_mult = 2 (default)
    User-controlled multiplier for QuickHull trigger threshold; chosen 'in practice' (§2.3) and not derived; affects candidate set size and runtime, but not the central claim.
  • pruning_offset = 1 (default)
    User-controlled offset for QuickHull trigger; same as above.
  • Monte Carlo threshold = 31.221 in §4.1; 32.796 in §5.1
    Thresholds calibrated by simulation quantiles to control false-positive rate in examples; not part of algorithm derivation.
  • anomaly_intensity = NULL (disabled); 1.5 in examples
    User-set minimum signal intensity to suppress baseline drift; not fitted, but a hand-picked knob for anomaly mode.
assumptions (5)
  • standard math A point in the interior of a convex hull can be written as a convex combination of d+2 hull points, so it cannot maximize a linear functional.
    Used in proof of Proposition 1 (Section 2.2).
  • domain assumption Data are generated i.i.d. from a one-parameter exponential family before and after the change, with independence across dimensions.
    The GLR and log^d complexity claims are for this model class; violations (e.g., dependence, non-exponential family) are outside the core claim.
  • domain assumption The expected number of convex hull vertices of the P(τ) random walk is O(log(n)^d), as stated in Corollary 1 of Pishchagina et al. (2025).
    This is the source of the per-iteration complexity claim; the paper does not reprove it.
  • domain assumption Pruning never discards a candidate that can be the GLR maximizer: D_n ⊆ D_{n-1} ∪ {n} and vertices of the hull suffice.
    Central to exactness; proof is sketched in Proposition 1 but inherited from prior work.
  • domain assumption For the AR(p) detector, AR coefficients and residual variance are known or estimated over a training period; Fan et al. (2026) provides the extension.
    Section 2.4 and 4.5; if coefficients are mis-estimated, the GLR is not exact for the true model.

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Cite this review

Pith. "Pith review of focus and focus-cpt: Fast Online Changepoint Detection in R and Python." pith.science (2026). https://pith.science/paper/MTVFGRNR

@misc{pith2026260719961,
  author       = {Pith},
  title        = {Pith review of: focus and focus-cpt: Fast Online Changepoint Detection in R and Python},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MTVFGRNR}},
  note         = {Machine review of arXiv:2607.19961}
}
abstract

We present an R and Python package for fast online changepoint detection in univariate and multivariate data streams for a variety of models. The package implements the focus family of algorithms, which compute the Generalised Likelihood Ratio test for a single changepoint exactly and efficiently, with a per-iteration cost of approximately $\log(n)^d$ for a d-dimensional sequence, without introducing approximations. This is achieved by exploiting a connection between the location of the changepoint candidates and the geometry of the data. The package supports a broad range of models from the natural exponential family, including Gaussian, Poisson, Binomial, Exponential and Gamma distributions, as well as a non-parametric detector based on the empirical cumulative distribution function and a detector for autoregressive data.

Figures

Figures reproduced from arXiv: 2607.19961 by the authors.

Figure 1
Figure 1. The trace of the statistics for one simulated time series using the R [PITH_FULL_IMAGE:figures/full_fig_p018_1.png] view at source ↗
Figure 2
Figure 2. The trace of the statistics for one simulated 3-dimensional multivariate time series [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
Figure 3
Figure 3. Comparison of statistics with and without minimum change size under a slowly [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison of sum and max statistics for two different change scenarios. Left: [PITH_FULL_IMAGE:figures/full_fig_p024_4.png]
Figure 5
Figure 5. Figure 5: The AR(2) process with mean shift and the resulting detection statistic using the [PITH_FULL_IMAGE:figures/full_fig_p025_5.png]
Figure 6
Figure 6. Figure 6: The NBA plus-minustest data (top) and the resulting test statistics (bottom). [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]
Figure 7
Figure 7. Figure 7: Online detection of a gamma-ray burst from Fermi-GBM data. Top: radiation [PITH_FULL_IMAGE:figures/full_fig_p029_7.png]
Figure 8
Figure 8. Figure 8: The trace of fluorescence intensity over time from a calcium imaging recording of [PITH_FULL_IMAGE:figures/full_fig_p031_8.png]
Figure 9
Figure 9. Figure 9: The trace of the statistics for one simulated time series using the Python interface. [PITH_FULL_IMAGE:figures/full_fig_p038_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

234 extracted references · 13 canonical work pages

  1. [1]

    and Killick, Rebecca , year =

    Hyndman, Rob J. and Killick, Rebecca , year =

  2. [2]

    2026 , eprint=

    An Efficient Likelihood Ratio Test for Online Changepoint Detection in the Presence of Autocorrelation , author=. 2026 , eprint=

    2026

  3. [3]

    Journal of Statistical Software , volume=

    cpop: detecting changes in piecewise-linear signals , author=. Journal of Statistical Software , volume=

  4. [4]

    Journal of Computational and Graphical Statistics , volume=

    Efficient implementations of the generalized lasso dual path algorithm , author=. Journal of Computational and Graphical Statistics , volume=. 2016 , publisher=

    2016

  5. [5]

    SIAM review , volume=

    ell\_1 trend filtering , author=. SIAM review , volume=. 2009 , publisher=

    2009

  6. [6]

    and Romano, Gaetano , year =

    Talagala, Priyanga Dilini and Hyndman, Rob J. and Romano, Gaetano , year =

  7. [7]

    2024 , note =

    breakfast: Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation , author =. 2024 , note =

    2024

  8. [8]

    Journal of statistical Software , volume=

    bcp: an R package for performing a Bayesian analysis of change point problems , author=. Journal of statistical Software , volume=

Show all 234 references
  1. [9]

    IEEE transactions on biomedical circuits and systems , volume=

    FPGA-based in-vivo calcium image decoding for closed-loop feedback applications , author=. IEEE transactions on biomedical circuits and systems , volume=. 2023 , publisher=

    2023

  2. [10]

    arXiv preprint arXiv:0710.3742 , year=

    Bayesian online changepoint detection , author=. arXiv preprint arXiv:0710.3742 , year=

    arXiv

  3. [11]

    Neurophotonics , volume=

    Closed-loop experiments and brain machine interfaces with multiphoton microscopy , author=. Neurophotonics , volume=. 2024 , publisher=

    2024

  4. [12]

    2019 , note =

    ocp: Bayesian Online Changepoint Detection , author =. 2019 , note =

    2019

  5. [13]

    Cell Reports Methods , volume=

    A real-time all-optical interface for dynamic perturbation of neural activity during behavior , author=. Cell Reports Methods , volume=. 2025 , publisher=

    2025

  6. [14]

    Mathematica Scandinavica , pages=

    On the fluctuations of sums of random variables II , author=. Mathematica Scandinavica , pages=. 1955 , publisher=

    1955

  7. [15]

    Some minorants and majorants of random walks and L

    Abramson, Joshua Simon , year=. Some minorants and majorants of random walks and L

  8. [16]

    Procedures for Reacting to a Change in Distribution

    Lorden, G. Procedures for Reacting to a Change in Distribution. Ann. Math. Statist. 1971. doi:10.1214/aoms/1177693055

    1971

  9. [17]

    Ross and Niall M

    Gordon J. Ross and Niall M. Adams , title =. Journal of Quality Technology , volume =. 2012 , publisher =

    2012

  10. [18]

    Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2 , pages =

    Khaleghi, Azadeh and Ryabko, Daniil , title =. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2 , pages =. 2012 , publisher =

    2012

  11. [19]

    Scott , title =

    Oscar Hernan Madrid Padilla and Alex Athey and Alex Reinhart and James G. Scott , title =. Journal of the American Statistical Association , volume =. 2019 , publisher =

    2019

  12. [20]

    Hawkins and Qiqi Deng , title =

    Douglas M. Hawkins and Qiqi Deng , title =. Journal of Quality Technology , volume =. 2010 , publisher =

    2010

  13. [21]

    Ross and Dimitris K

    Gordon J. Ross and Dimitris K. Tasoulis and Niall M. Adams , title =. Technometrics , volume =. 2011 , publisher =

    2011

  14. [22]

    and Tartakovsky, Alexander G

    Polunchenko, Aleksey S. and Tartakovsky, Alexander G. State-of-the-Art in Sequential Change-Point Detection. Methodology and Computing in Applied Probability. 2012. doi:10.1007/s11009-011-9256-5

    2012 doi

  15. [23]

    1961 , journal=

    PROBLEM OF MOST RAPID DETECTION OF A DISTURBANCE IN StATIONARY PROCESSES , author=. 1961 , journal=

    1961

  16. [24]

    Communications in Statistics - Theory and Methods , volume =

    Dabuxilatu Wang and Liang Zhang and Qiang Xiong , title =. Communications in Statistics - Theory and Methods , volume =. 2017 , publisher =

    2017

  17. [25]

    Sequential Analysis: Hypothesis Testing and Changepoint Detection , isbn =

    Tartakovsky, Alexander and Nikiforov, Igor and Basseville, Michèle , year =. Sequential Analysis: Hypothesis Testing and Changepoint Detection , isbn =

  18. [26]

    Biometrika , volume=

    Continuous inspection schemes , author=. Biometrika , volume=. 1954 , publisher=

    1954

  19. [27]

    Knowledge and Information Systems , year=

    A survey of methods for time series change point detection , author=. Knowledge and Information Systems , year=

  20. [28]

    Signal Process

    A review of change point detection methods , author=. Signal Process. , year=

  21. [29]

    Signal processing , volume=

    Selective review of offline change point detection methods , author=. Signal processing , volume=. 2020 , publisher=

    2020

  22. [30]

    Real-time financial surveillance via quickest change-point detection methods , volume =

    Pepelyshev, Andrey and Polunchenko, Aleksey , year =. Real-time financial surveillance via quickest change-point detection methods , volume =. Statistics and its interface , doi =

  23. [31]

    Data Science , volume=

    Predictive maintenance solution for industrial systems—an unsupervised approach based on log periodic power law , author=. Data Science , volume=. 2025 , publisher=

    2025

  24. [32]

    Sequential change-point detection based on nearest neighbors

    Chen, Hao. Sequential change-point detection based on nearest neighbors. Ann. Statist. 2019. doi:10.1214/18-AOS1718

    2019 doi

  25. [33]

    Change-Points: From Sequential Detection to Biology and Back , year =

    David Siegmund , journal =. Change-Points: From Sequential Detection to Biology and Back , year =. doi:10.1080/07474946.2013.751834 , publisher =

    2013

  26. [34]

    and Arabhi, S

    Jackson, Brad and Scargle, Jeffrey and Barnes, D. and Arabhi, S. and Alt, A. and Gioumousis, P. and Gwin, E. and Sangtrakulcharoen, P. and Tan, L. and Tsai, Tun , year =. An algorithm for optimal partitioning of data on an interval , volume =. Signal Processing Letters, IEEE , doi =

  27. [35]

    ACM Transactions on Mathematical Software (TOMS) , volume=

    The quickhull algorithm for convex hulls , author=. ACM Transactions on Mathematical Software (TOMS) , volume=. 1996 , publisher=

    1996

  28. [36]

    The Annals of Statistics , volume=

    Nonparametric maximum likelihood approach to multiple change-point problems , author=. The Annals of Statistics , volume=. 2014 , publisher=

    2014

  29. [37]

    A Note on Bernoulli Trials with Dependence , volume =

    Bo Lindqvist , journal =. A Note on Bernoulli Trials with Dependence , volume =

  30. [38]

    2014 , publisher=

    An Introduction to Statistical Modeling of Extreme Values , author=. 2014 , publisher=

    2014

  31. [39]

    2013 , publisher=

    Modelling Extremal Events: for Insurance and Finance , author=. 2013 , publisher=

    2013

  32. [40]

    Extreme value theory in mixture distributions and a statistical method to control the possible bias

    Wonseon Gwak and Hyein Goo and Yang Ho Choi and Jae Youn Ahn. Extreme value theory in mixture distributions and a statistical method to control the possible bias. Journal of the Korean Statistical Society. 2016. doi:https://doi.org/10.1016/j.jkss.2016.04.003

    2016 doi

  33. [41]

    Approximated distributions of the weighted sum of correlated chi-squared random variables

    Li-Ling Chuang and Yu-Shan Shih. Approximated distributions of the weighted sum of correlated chi-squared random variables. Journal of Statistical Planning and Inference. 2012. doi:https://doi.org/10.1016/j.jspi.2011.08.004

    2012 doi

  34. [42]

    A pruned dynamic programming algorithm to recover the best segmentations with 1 to Kmax change-points , year=

    Guillem Rigaill , journal=. A pruned dynamic programming algorithm to recover the best segmentations with 1 to Kmax change-points , year=

  35. [43]

    Changepoint Detection in the Presence of Outliers

    Paul Fearnhead and Guillem Rigaill. Changepoint Detection in the Presence of Outliers. Journal of the American Statistical Association. 2019

    2019

  36. [44]

    On optimal multiple changepoint algorithms for large data

    Maidstone, Robert and Hocking, Toby and Rigaill, Guillem and Fearnhead, Paul. On optimal multiple changepoint algorithms for large data. Statistics and Computing. 2017

    2017

  37. [45]

    arXiv preprint arXiv:1703.03352 , year=

    A log-linear time algorithm for constrained changepoint detection , author=. arXiv preprint arXiv:1703.03352 , year=

    arXiv

  38. [46]

    Journal of Statistical Software , author=

    gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection , volume=. Journal of Statistical Software , author=. 2023 , pages=. doi:10.18637/jss.v106.i06 , number=

    2023 doi

  39. [47]

    Journal of the American Statistical Association , pages=

    Detecting abrupt changes in the presence of local fluctuations and autocorrelated noise , author=. Journal of the American Statistical Association , pages=. 2021 , publisher=

    2021

  40. [48]

    arXiv preprint arXiv:1801.00826 , year=

    ruptures: change point detection in Python , author=. arXiv preprint arXiv:1801.00826 , year=

    arXiv

  41. [49]

    2020 , note =

    ocd: High-Dimensional Multiscale Online Changepoint Detection , author =. 2020 , note =

    2020

  42. [50]

    Information Processing Letters , volume=

    On-line construction of the convex hull of a simple polyline , author=. Information Processing Letters , volume=. 1987 , publisher=

    1987

  43. [51]

    Electronic Journal of Statistics , volume=

    Modified sequential change point procedures based on estimating functions , author=. Electronic Journal of Statistics , volume=. 2018 , publisher=

    2018

  44. [52]

    arXiv preprint arXiv:2003.02682 , year=

    Backward CUSUM for testing and monitoring structural change , author=. arXiv preprint arXiv:2003.02682 , year=

    2003 arXiv

  45. [53]

    2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) , pages=

    Evaluating Real-Time anomaly detection algorithms--The Numenta anomaly benchmark , author=. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) , pages=. 2015 , organization=

    2015

  46. [54]

    Neurocomputing , volume=

    Unsupervised real-time anomaly detection for streaming data , author=. Neurocomputing , volume=. 2017 , publisher=

    2017

  47. [55]

    Neural computation , volume=

    Continuous online sequence learning with an unsupervised neural network model , author=. Neural computation , volume=. 2016 , publisher=

    2016

  48. [56]

    2004 , publisher=

    Robust statistics , author=. 2004 , publisher=

    2004

  49. [57]

    Journal of the American Statistical Association , volume=

    A likelihood ratio approach to sequential change point detection for a general class of parameters , author=. Journal of the American Statistical Association , volume=. 2020 , publisher=

    2020

  50. [58]

    Sequential Analysis , volume=

    A note on online change point detection , author=. Sequential Analysis , volume=. 2023 , publisher=

    2023

  51. [59]

    Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=

    High-dimensional, multiscale online changepoint detection , author=. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=. 2022 , publisher=

    2022

  52. [60]

    Biometrika , volume=

    The state of cumulative sum sequential changepoint testing 70 years after Page , author=. Biometrika , volume=. 2024 , publisher=

    2024

  53. [61]

    IEEE Transactions on Information Theory , volume=

    Window-limited CUSUM for sequential change detection , author=. IEEE Transactions on Information Theory , volume=. 2023 , publisher=

    2023

  54. [62]

    Biometrika , volume=

    A test for a change in a parameter occurring at an unknown point , author=. Biometrika , volume=. 1955 , publisher=

    1955

  55. [63]

    2015 Computing in Cardiology Conference (CinC) , pages=

    The PhysioNet/computing in cardiology challenge 2015: reducing false arrhythmia alarms in the ICU , author=. 2015 Computing in Cardiology Conference (CinC) , pages=. 2015 , organization=

    2015

  56. [64]

    arXiv preprint arXiv:1906.03225 , year=

    A new approach for open-end sequential change point monitoring , author=. arXiv preprint arXiv:1906.03225 , year=

    1906 arXiv

  57. [65]

    arXiv preprint arXiv:2009.06670 , year=

    Real Time Anomaly Detection And Categorisation , author=. arXiv preprint arXiv:2009.06670 , year=

    2009 arXiv

  58. [66]

    arXiv preprint arXiv:1608.04585 , year=

    Conformalized density-and distance-based anomaly detection in time-series data , author=. arXiv preprint arXiv:1608.04585 , year=

    arXiv

  59. [67]

    Econometrica: Journal of the Econometric Society , pages=

    Monitoring structural change , author=. Econometrica: Journal of the Econometric Society , pages=. 1996 , publisher=

    1996

  60. [68]

    2010 , pages=

    Gamma , author=. 2010 , pages=

    2010

  61. [69]

    Mathematics and computers in simulation , volume=

    Modified procedures for change point monitoring in linear models , author=. Mathematics and computers in simulation , volume=. 2010 , publisher=

    2010

  62. [70]

    Journal of Statistical Planning and Inference , volume=

    Monitoring changes in linear models , author=. Journal of Statistical Planning and Inference , volume=. 2004 , publisher=

    2004

  63. [71]

    Biometrika , volume=

    MOSUM tests for parameter constancy , author=. Biometrika , volume=. 1995 , publisher=

    1995

  64. [72]

    Technometrics , volume=

    The use of MOSUMS for quality control , author=. Technometrics , volume=. 1978 , publisher=

    1978

  65. [73]

    2010 , publisher=

    Structural health monitoring , author=. 2010 , publisher=

    2010

  66. [74]

    IEEE Transactions on signal processing , volume=

    Anomaly detection in IP networks , author=. IEEE Transactions on signal processing , volume=. 2003 , publisher=

    2003

  67. [75]

    International conference on research in networking , pages=

    Proactively detecting distributed denial of service attacks using source IP address monitoring , author=. International conference on research in networking , pages=. 2004 , organization=

    2004

  68. [76]

    Renewable and Sustainable energy reviews , volume=

    Condition monitoring and fault detection of wind turbines and related algorithms: A review , author=. Renewable and Sustainable energy reviews , volume=. 2009 , publisher=

    2009

  69. [77]

    2013 , publisher=

    Real time fault monitoring of industrial processes , author=. 2013 , publisher=

    2013

  70. [78]

    Mechanical Systems and Signal Processing , volume=

    In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition , author=. Mechanical Systems and Signal Processing , volume=. 2020 , publisher=

    2020

  71. [79]

    IEEE Control Systems Magazine , volume=

    In-flight vibration monitoring of aeronautical structures , author=. IEEE Control Systems Magazine , volume=. 2007 , publisher=

    2007

  72. [80]

    Monthly Notices of the Royal Astronomical Society , volume=

    A method of detecting radio transients , author=. Monthly Notices of the Royal Astronomical Society , volume=. 2010 , publisher=

    2010

  73. [81]

    2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) , pages=

    In Datacenter Performance, The Only Constant Is Change , author=. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) , pages=. 2020 , organization=

    2020

  74. [82]

    Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , volume=

    HERMES: An ultra-wide band X and gamma-ray transient monitor on board a nano-satellite constellation , author=. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , volume=. 2019 , publisher=

    2019

  75. [83]

    Mathematical finance—Bachelier congress 2000 , pages=

    Quickest detection problems in the technical analysis of the financial data , author=. Mathematical finance—Bachelier congress 2000 , pages=. 2002 , organization=

    2000

  76. [84]

    2019 IEEE International Symposium on Information Theory (ISIT) , pages=

    Asynchronous multi-sensor change-point detection for seismic tremors , author=. 2019 IEEE International Symposium on Information Theory (ISIT) , pages=. 2019 , organization=

    2019

  77. [85]

    New procedure for change detection operating on R

    Popescu, Theodor D and Aiordǎchioaie, Dorel , journal=. New procedure for change detection operating on R. 2017 , publisher=

    2017

  78. [86]

    2011 , publisher=

    Convex analysis and monotone operator theory in Hilbert spaces , author=. 2011 , publisher=

    2011

  79. [87]

    Bioinformatics , volume=

    Transcriptional landscape estimation from tiling array data using a model of signal shift and drift , author=. Bioinformatics , volume=. 2009 , publisher=

    2009

  80. [88]

    Press, Princeton, NJ , year=

    Applied Dynamic Programming, Princeton Unlv , author=. Press, Princeton, NJ , year=

  81. [89]

    and Ewans, Kevin and Jonathan, Philip , issn =

    Killick, Rebecca and Eckley, Idris A. and Ewans, Kevin and Jonathan, Philip , issn =. Ocean Engineering , month = sep, number =

  82. [90]

    and Lund, Robert and Lu, Qi Qi , doi =

    Reeves, Jaxk and Chen, Jien and Wang, Xiaolan L. and Lund, Robert and Lu, Qi Qi , doi =. Journal of Applied Meteorology and Climatology , month = jun, pages =

  83. [91]

    Biostatistics , month = oct, pages =

    Olshen, Adam B and Venkatraman, E S and Lucito, Robert and Wigler, Michael , doi =. Biostatistics , month = oct, pages =

  84. [92]

    Journal of statistical software , volume=

    changepoint: An R package for changepoint analysis , author=. Journal of statistical software , volume=

  85. [93]

    Killick and P

    R. Killick and P. Fearnhead and I. A. Eckley , title =. Journal of the American Statistical Association , volume =. 2012 , publisher =

    2012

  86. [94]

    2013 , vol=

    Frontiers in massive data analysis , author=. 2013 , vol=

    2013

  87. [95]

    The Annals of Statistics , volume=

    Tail-greedy bottom-up data decompositions and fast multiple change-point detection , author=. The Annals of Statistics , volume=. 2018 , publisher=

    2018

  88. [96]

    2006 , publisher=

    Linear model theory: univariate, multivariate, and mixed models , author=. 2006 , publisher=

    2006

  89. [97]

    Journal of the Royal Statistical Society: Series B , number =

    Frick, Klaus and Munk, Axel and Sieling, Hannes , eprint =. Journal of the Royal Statistical Society: Series B , number =

  90. [98]

    The Annals of Statistics , year=2000, number=5, volume=28, pages=

    Adaptive Estimation of a Quadratic Functional by Model Selection , author=. The Annals of Statistics , year=2000, number=5, volume=28, pages=

    2000

  91. [99]

    Consistency of a range of penalised cost approaches for detecting multiple changepoints , author=

  92. [100]

    Sequential Tests of Statistical Hypotheses

    Wald, A. Sequential Tests of Statistical Hypotheses. Ann. Math. Statist. 1945. doi:10.1214/aoms/1177731118

    1945

  93. [101]

    IEEE Transactions on Nanobioscience , volume=

    Idealizing ion channel recordings by a jump segmentation multiresolution filter , author=. IEEE Transactions on Nanobioscience , volume=. 2013 , publisher=

    2013

  94. [102]

    Bioinformatics , volume =

    Futschik, Andreas and Hotz, Thomas and Munk, Axel and Sieling, Hannes , title =. Bioinformatics , volume =. 2014 , doi =

    2014

  95. [103]

    Manuela Gonçalves and Lara Teixeira , title =

    Marco Costa and A. Manuela Gonçalves and Lara Teixeira , title =. Electronic Journal of Applied Statistical Analysis , volume =. 2016 , issn =

    2016

  96. [104]

    Journal of Machine Learning Research , year=

    Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data , author=. Journal of Machine Learning Research , year=

  97. [105]

    and Lawrence, Charles E

    Auger, Ivan E. and Lawrence, Charles E. Algorithms for the optimal identification of segment neighborhoods. Bulletin of Mathematical Biology. 1989. doi:10.1007/BF02458835

    1989 doi

  98. [106]

    1988 , publisher=

    Yao, Yi-Ching , journal=. 1988 , publisher=

    1988

  99. [107]

    Journal of Computational and Graphical Statistics , pages=

    Parallelization of a Common Changepoint Detection Method , author=. Journal of Computational and Graphical Statistics , pages=. 2020 , publisher=

    2020

  100. [108]

    Estimation of a Noisy Discrete-Time Step Function: Bayes and Empirical Bayes Approaches

    Yao, Yi-Ching. Estimation of a Noisy Discrete-Time Step Function: Bayes and Empirical Bayes Approaches. Ann. Statist. 1984. doi:10.1214/aos/1176346802

    1984

  101. [109]

    IEEE Signal Processing Letters , volume=

    An algorithm for optimal partitioning of data on an interval , author=. IEEE Signal Processing Letters , volume=. 2005 , publisher=

    2005

  102. [110]

    2012 , publisher=

    Numerical Bayesian methods applied to signal processing , author=. 2012 , publisher=

    2012

  103. [111]

    2011 , publisher=

    Fearnhead, Paul and Liu, Zhen , journal=. 2011 , publisher=

    2011

  104. [112]

    Eckley and Paul Fearnhead , title =

    Kaylea Haynes and Idris A. Eckley and Paul Fearnhead , title =. Journal of Computational and Graphical Statistics , volume =. 2017 , publisher =. doi:10.1080/10618600.2015.1116445 , eprint =

    2017

  105. [113]

    2014 , volume=42, pages=

    Fryzlewicz, Piotr , journal =. 2014 , volume=42, pages=

    2014

  106. [114]

    Journal of Business & Economic Statistics , year=

    The structural break in the equity premium , author=. Journal of Business & Economic Statistics , year=

  107. [115]

    Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume =

    Cho, Haeran and Fryzlewicz, Piotr , title =. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume =. doi:10.1111/rssb.12079 , url =. https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssb.12079 , year =

    doi

  108. [116]

    Advances in Neural Information Processing Systems 23 , editor =

    Fast detection of multiple change-points shared by many signals using group LARS , author =. Advances in Neural Information Processing Systems 23 , editor =. 2010 , publisher =

    2010

  109. [117]

    and Alharbi, Basma and Wang, Suojin and Zhang, Xiangliang , title =

    Qahtan, Abdulhakim A. and Alharbi, Basma and Wang, Suojin and Zhang, Xiangliang , title =. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , series =. 2015 , isbn =. doi:10.1145/2783258.2783359 , acmid =

    2015

  110. [118]

    Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection , author=

  111. [119]

    Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

    Cabrieto, Jedelyn and Tuerlinckx, Francis and Kuppens, Peter and Grassmann, Mariel and Ceulemans, Eva. Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods. Behavior Research Methods. 2017. doi:10.3758/s1...

    2017 doi

  112. [120]

    A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening , volume =

    Lin, Kevin and Sharpnack, James L and Rinaldo, Alessandro and Tibshirani, Ryan J , booktitle =. A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening , volume =

  113. [121]

    Journal of the American Statistical Association , number=

    Joint structural break detection and parameter estimation in high-dimensional non-stationary var models , author=. Journal of the American Statistical Association , number=. 2020 , publisher=

    2020

  114. [122]

    Journal of Computational and Graphical Statistics , pages=

    Detecting Changes in Slope With an L0 Penalty , author=. Journal of Computational and Graphical Statistics , pages=. 2018 , publisher=

    2018

  115. [123]

    arXiv:1901.07277 , year=

    Minimal penalties and the slope heuristics: a survey , author=. arXiv:1901.07277 , year=

    1901 arXiv

  116. [124]

    2016 , note =

    capushe: calibrating penalities using slope heuristics , author =. 2016 , note =

    2016

  117. [125]

    Journal of Statistical Software, Articles , volume =

    Nicholas James and David Matteson , title =. Journal of Statistical Software, Articles , volume =. 2015 , keywords =. doi:10.18637/jss.v062.i07 , url =

    2015 doi

  118. [126]

    arXiv:1202.3878 , volume=

    Kernel change-point detection , author=. arXiv:1202.3878 , volume=. 2012 , publisher=

    2012 arXiv

  119. [127]

    IEEE Transactions on Information Theory , volume=

    A dirty model for multiple sparse regression , author=. IEEE Transactions on Information Theory , volume=. 2013 , publisher=

    2013

  120. [128]

    New efficient algorithms for multiple change-point detection with kernels , journal =

  121. [129]

    The Annals of Statistics , volume=

    Adaptive piecewise polynomial estimation via trend filtering , author=. The Annals of Statistics , volume=. 2014 , publisher=

    2014

  122. [130]

    Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume=

    Sparsity and smoothness via the fused lasso , author=. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume=. 2005 , publisher=

    2005

  123. [131]

    The Annals of Statistics , eprint =

    Chernozhukov, Victor and Hansen, Christian and Liao, Yuan , title =. The Annals of Statistics , eprint =

  124. [132]

    Is a Finite Intersection of Balls Covered by a Finite Union of Balls in Euclidean Spaces ? , journal =

  125. [133]

    The Annals of Applied Statistics , volume=

    Exact spike train inference via L_0 optimization , author=. The Annals of Applied Statistics , volume=. 2018 , publisher=

    2018

  126. [134]

    Technometrics , volume=

    Most recent changepoint detection in panel data , author=. Technometrics , volume=. 2019 , publisher=

    2019

  127. [135]

    Probability and Mathematical Statistics , volume=

    Inequalities for quantiles of the chi-square distribution , author=. Probability and Mathematical Statistics , volume=

  128. [136]

    arXiv:1803.04516 , year=

    Explicit inverse of tridiagonal matrix with applications in autoregressive modeling , author=. arXiv:1803.04516 , year=

    arXiv

  129. [137]

    Bernoulli , volume=

    Chakar, Souhil and Lebarbier, E and L. Bernoulli , volume=. 2017 , publisher=

    2017

  130. [138]

    Journal of Time Series Analysis , volume=

    Least-squares estimation of an unknown number of shifts in a time series , author=. Journal of Time Series Analysis , volume=. 2000 , publisher=

    2000

  131. [139]

    Biometrika , volume=

    Inference about the change-point from cumulative sum tests , author=. Biometrika , volume=. 1971 , publisher=

    1971

  132. [140]

    Journal of the American Statistical Association , volume=

    Alternatives to the median absolute deviation , author=. Journal of the American Statistical Association , volume=. 1993 , publisher=

    1993

  133. [141]

    2020 , url =

    R: A Language and Environment for Statistical Computing , author =. 2020 , url =

    2020

  134. [142]

    2020 , note =

    DeCAFS: detecting changes in autocorrelated and fluctuating signals , author =. 2020 , note =

    2020

  135. [143]

    Tartakovsky and Boris Lʹvovich Rozovskiĭ , booktitle =

    Alexander G. Tartakovsky and Boris Lʹvovich Rozovskiĭ , booktitle =. A nonparametric multichart CUSUM test for rapid intrusion detection , year =

  136. [144]

    , journal =

    Haynes, Kaylea and Fearnhead, Paul and Eckley, Idris A. , journal =. A computationally efficient nonparametric approach for changepoint detection , year =. doi:10.1007/s11222-016-9687-5 , issue_date =

    doi

  137. [145]

    A MOSUM procedure for the estimation of multiple random change points , year =

    Eichinger, Birte and Kirch, Claudia , journal =. A MOSUM procedure for the estimation of multiple random change points , year =. doi:10.3150/16-BEJ887 , fjournal =

    doi

  138. [146]

    Killick and P

    R. Killick and P. Fearnhead and I. A. Eckley , journal =. Optimal Detection of Changepoints With a Linear Computational Cost , year =. doi:10.1080/01621459.2012.737745 , eprint =

    2012

  139. [147]

    On tests for detecting change in mean , year =

    Sen, Ashish and Srivastava, Muni S , journal =. On tests for detecting change in mean , year =

  140. [148]

    A cluster analysis method for grouping means in the analysis of variance , year =

    Scott, Andrew Jhon and Knott, M , journal =. A cluster analysis method for grouping means in the analysis of variance , year =

  141. [149]

    Fast nonconvex deconvolution of calcium imaging data , year =

    Jewell, Sean W and Hocking, Toby Dylan and Fearnhead, Paul and Witten, Daniela M , journal =. Fast nonconvex deconvolution of calcium imaging data , year =. doi:10.1093/biostatistics/kxy083 , publisher =

    doi

  142. [150]

    PloS one , volume=

    Inferring spikes from calcium imaging in dopamine neurons , author=. PloS one , volume=. 2021 , publisher=

    2021

  143. [151]

    Least-squares estimation of a step function , author=. Sankhy. 1989 , publisher=

    1989

  144. [152]

    arXiv preprint arXiv:1004.0887 , volume=

    Pruned dynamic programming for optimal multiple change-point detection , author=. arXiv preprint arXiv:1004.0887 , volume=. 2010 , publisher=

    2010 arXiv

  145. [153]

    arXiv preprint arXiv:2012.11573 , year=

    Change-in-Slope Optimal Partitioning Algorithm in a Finite-Size Parameter Space , author=. arXiv preprint arXiv:2012.11573 , year=

    2012 arXiv

  146. [154]

    arXiv preprint arXiv:1909.01691 , year=

    Subset multivariate collective and point anomaly detection , author=. arXiv preprint arXiv:1909.01691 , year=

    1909 arXiv

  147. [155]

    Journal of Computational and Graphical Statistics , volume=

    Parallelization of a common changepoint detection method , author=. Journal of Computational and Graphical Statistics , volume=. 2020 , publisher=

    2020

  148. [156]

    Biometrika , volume=

    Seeded binary segmentation: a general methodology for fast and optimal changepoint detection , author=. Biometrika , volume=. 2023 , publisher=

    2023

  149. [157]

    Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume=

    High dimensional change point estimation via sparse projection , author=. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume=. 2018 , publisher=

    2018

  150. [158]

    Applied and Computational Harmonic Analysis , volume=

    High-dimensional change-point estimation: Combining filtering with convex optimization , author=. Applied and Computational Harmonic Analysis , volume=. 2017 , publisher=

    2017

  151. [159]

    Journal of the Royal Statistical Society

    Change point estimation in high dimensional Markov random-field models , author=. Journal of the Royal Statistical Society. Series B, Statistical methodology , volume=. 2017 , publisher=

    2017

  152. [160]

    arXiv preprint arXiv:1601.03704 , year=

    Computationally efficient change point detection for high-dimensional regression , author=. arXiv preprint arXiv:1601.03704 , year=

    arXiv

  153. [161]

    Journal of the Royal Statistical Society: Series B: Statistical Methodology , pages=

    Multiple-change-point detection for high dimensional time series via sparsified binary segmentation , author=. Journal of the Royal Statistical Society: Series B: Statistical Methodology , pages=. 2015 , publisher=

    2015

  154. [162]

    Statistica Sinica , pages=

    Multiple change-point detection for non-stationary time series using wild binary segmentation , author=. Statistica Sinica , pages=. 2017 , publisher=

    2017

  155. [163]

    Journal of the Royal Statistical Society: Series B: Statistical Methodology , pages=

    Multiple-change-point detection for auto-regressive conditional heteroscedastic processes , author=. Journal of the Royal Statistical Society: Series B: Statistical Methodology , pages=. 2014 , publisher=

    2014

  156. [164]

    arXiv preprint arXiv:1609.00293 , year=

    Narrowest-over-threshold detection of multiple change-points and change-point-like features , author=. arXiv preprint arXiv:1609.00293 , year=

    arXiv

  157. [165]

    2021 , number =

    Oscar Hernan Madrid Padilla and Yi Yu and Daren Wang and Alessandro Rinaldo , journal =. 2021 , number =. doi:10.1214/21-EJS1809 , keywords =

    2021 doi

  158. [166]

    , pages =

    Vershynin, R. , pages =. High-dimensional probability: an introduction with applications in data science , year =

  159. [167]

    A nonparametric control chart based on the Mood statistic for dispersion , year =

    Hidetoshi Murakami and Takashi Matsuki , journal =. A nonparametric control chart based on the Mood statistic for dispersion , year =

  160. [168]

    , journal =

    Chakraborti, Subhabrata and van de Wiel, Mark A. , journal =. A nonparametric control chart based on the Mann-Whitney statistic , year =. doi:10.1214/193940307000000112 , isbn =

    doi

  161. [169]

    1994 , number =

    Louis Gordon and Moshe Pollak , journal =. 1994 , number =. doi:10.1214/aos/1176325495 , keywords =

    1994

  162. [170]

    A Sequential Rank-Based Nonparametric Adaptive EWMA Control Chart , year =

    Liu Liu and Xuemin Zi and Jian Zhang and Zhaojun Wang , journal =. A Sequential Rank-Based Nonparametric Adaptive EWMA Control Chart , year =. doi:10.1080/03610918.2012.655829 , publisher =

    2012

  163. [171]

    and Chakraborti, S

    Mukherjee, A. and Chakraborti, S. , journal =. A Distribution-free Control Chart for the Joint Monitoring of Location and Scale , year =

  164. [172]

    and Graham, M.A

    Coelho, M.L.I. and Graham, M.A. and Chakraborti, S. , journal =. Nonparametric signed-rank control charts with variable sampling intervals , year =. doi:https://doi.org/10.1002/qre.2177 , keywords =

    doi

  165. [173]

    Ross , journal =

    Gordon J. Ross , journal =. Parametric and Nonparametric Sequential Change Detection in R: The cpm Package , year =. doi:10.18637/jss.v066.i03 , url =

    doi

  166. [174]

    mosum: A Package for Moving Sums in Change-Point Analysis , year =

    Alexander Meier and Claudia Kirch and Haeran Cho , journal =. mosum: A Package for Moving Sums in Change-Point Analysis , year =. doi:10.18637/jss.v097.i08 , keywords =

    doi

  167. [175]

    , publisher =

    Wainwright, M.J. , publisher =. High-dimensional statistics: a non-asymptotic viewpoint , year =

  168. [176]

    S. S. Wilks , journal =. 1938 , number =. doi:10.1214/aoms/1177732360 , publisher =

    1938

  169. [177]

    2019 , publisher=

    Estimating multiple structural breaks in time series-a generalized MOSUM approach based on estimating functions , author=. 2019 , publisher=

    2019

  170. [178]

    Statistics and computing , volume=

    Exact and efficient Bayesian inference for multiple changepoint problems , author=. Statistics and computing , volume=. 2006 , publisher=

    2006

  171. [179]

    Journal of the American Statistical Association , volume=

    A Bayesian analysis for change point problems , author=. Journal of the American Statistical Association , volume=. 1993 , publisher=

    1993

  172. [180]

    , author=

    Bayesian inference on biopolymer models. , author=. Bioinformatics (Oxford, England) , volume=

  173. [181]

    Biometrika , volume=

    Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , author=. Biometrika , volume=. 1995 , publisher=

    1995

  174. [182]

    Biometrika , pages=

    Bayesian analysis of a Poisson process with a change-point , author=. Biometrika , pages=. 1986 , publisher=

    1986

  175. [183]

    arXiv preprint arXiv:1106.4199 , year=

    The group fused lasso for multiple change-point detection , author=. arXiv preprint arXiv:1106.4199 , year=

    arXiv

  176. [184]

    , author=

    Catching Change-points with Lasso. , author=. NIPS , volume=

  177. [185]

    arXiv preprint arXiv:2107.01742 , year=

    Nonparametric detection of multiple location-scale change points via wild binary segmentation , author=. arXiv preprint arXiv:2107.01742 , year=

    arXiv

  178. [186]

    The Annals of Mathematical Statistics , pages=

    On the asymptotic efficiency of certain nonparametric two-sample tests , author=. The Annals of Mathematical Statistics , pages=. 1954 , publisher=

    1954

  179. [187]

    Quality and Reliability Engineering International , volume=

    Change-point detection for shifts in control charts using EM change-point algorithms , author=. Quality and Reliability Engineering International , volume=. 2016 , publisher=

    2016

  180. [188]

    Annals of statistics , pages=

    Bayesian analysis of mixture models with an unknown number of components-an alternative to reversible jump methods , author=. Annals of statistics , pages=. 2000 , publisher=

    2000

  181. [189]

    Journal of Computational and Graphical Statistics , volume=

    Simultaneous credible regions for multiple changepoint locations , author=. Journal of Computational and Graphical Statistics , volume=. 2019 , publisher=

    2019

  182. [190]

    2018 , publisher=

    High-dimensional probability: an introduction with applications in data science , author=. 2018 , publisher=

    2018

  183. [191]

    2019 , publisher=

    High-dimensional statistics: a non-asymptotic viewpoint , author=. 2019 , publisher=

    2019

  184. [192]

    Journal of Optimization Theory and Applications , volume=

    Is a Finite Intersection of Balls Covered by a Finite Union of Balls in Euclidean Spaces? , author=. Journal of Optimization Theory and Applications , volume=. 2020 , publisher=

    2020

  185. [193]

    Applied Stochastic Models in Business and Industry , volume=

    Statistical methods for network surveillance , author=. Applied Stochastic Models in Business and Industry , volume=. 2018 , publisher=

    2018

  186. [194]

    IEEE Journal of Selected Topics in Signal Processing , volume=

    Efficient computer network anomaly detection by changepoint detection methods , author=. IEEE Journal of Selected Topics in Signal Processing , volume=. 2012 , publisher=

    2012

  187. [195]

    Technometrics , volume=

    Approximations to the average run length in cumulative sum control charts , author=. Technometrics , volume=. 1975 , publisher=

    1975

  188. [196]

    Journal of Statistical Planning and Inference , volume=

    On the use of estimating functions in monitoring time series for change points , author=. Journal of Statistical Planning and Inference , volume=. 2015 , publisher=

    2015

  189. [197]

    Journal of the Korean Statistical Society , volume=

    Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection , author=. Journal of the Korean Statistical Society , volume=. 2020 , publisher=

    2020

  190. [198]

    Computational Statistics & Data Analysis , volume=

    Joint segmentation of multivariate Gaussian processes using mixed linear models , author=. Computational Statistics & Data Analysis , volume=. 2011 , publisher=

    2011

  191. [199]

    Biometrika , volume=

    Maximum likelihood estimation via the ECM algorithm: A general framework , author=. Biometrika , volume=. 1993 , publisher=

    1993

  192. [200]

    Journal of the Royal Statistical Society: Series C (Applied Statistics) , volume=

    Bayesian retrospective multiple-changepoint identification , author=. Journal of the Royal Statistical Society: Series C (Applied Statistics) , volume=. 1994 , publisher=

    1994

  193. [201]

    arXiv preprint arXiv:1806.01947 , year=

    A linear time method for the detection of point and collective anomalies , author=. arXiv preprint arXiv:1806.01947 , year=

    arXiv

  194. [202]

    How to travel with a salmon: and other essays , chapter=

    Eco, Umberto , year=. How to travel with a salmon: and other essays , chapter=

  195. [203]

    NeurIPS-2021 , title =

    2021

  196. [204]

    1993 , publisher=

    Detection of abrupt changes: theory and application , author=. 1993 , publisher=

    1993

  197. [205]

    arXiv preprint arXiv:2210.07066 , year=

    Detecting A Single Change-point , author=. arXiv preprint arXiv:2210.07066 , year=

    arXiv

  198. [206]

    arXiv preprint arXiv:2210.05181 , year=

    Sequential change-point detection: Computation versus statistical performance , author=. arXiv preprint arXiv:2210.05181 , year=

    arXiv

  199. [207]

    Journal of the American Statistical Association , pages=

    Poisson-FOCuS: An efficient online method for detecting count bursts with application to gamma ray burst detection , author=. Journal of the American Statistical Association , pages=. 2023 , publisher=

    2023

  200. [208]

    IEEE Transactions on Signal Processing , year=

    A log-linear non-parametric online changepoint detection algorithm based on functional pruning , author=. IEEE Transactions on Signal Processing , year=

  201. [209]

    The Annals of Statistics , volume=

    High-dimensional change-point detection under sparse alternatives , author=. The Annals of Statistics , volume=. 2019 , publisher=

    2019

  202. [210]

    Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment

    Galceran, Enric and Cunningham, Alexander and Eustice, Ryan and Olson, Edwin , year =. Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment. , volume =. Autonomous Robots , doi =

  203. [211]

    Journal of Computational and Graphical Statistics , pages=

    Subset multivariate collective and point anomaly detection , author=. Journal of Computational and Graphical Statistics , pages=. 2021 , publisher=

    2021

  204. [212]

    Experimental Astronomy , volume=

    Searching for long faint astronomical high energy transients: a data driven approach , author=. Experimental Astronomy , volume=. 2023 , publisher=

    2023

  205. [213]

    Journal of Statistical Software , volume=

    Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data , author=. Journal of Statistical Software , volume=

  206. [214]

    Computational Statistics & Data Analysis , volume=

    Online non-parametric changepoint detection with application to monitoring operational performance of network devices , author=. Computational Statistics & Data Analysis , volume=. 2023 , publisher=

    2023

  207. [215]

    European Journal of Soil Science , volume=

    Changes in microbial utilization and fate of soil carbon following the addition of different fractions of anaerobic digestate to soils , author=. European Journal of Soil Science , volume=. 2021 , publisher=

    2021

  208. [216]

    Journal of the American Statistical Association , number=

    Inference in high-dimensional online changepoint detection , author=. Journal of the American Statistical Association , number=. 2023 , publisher=

    2023

  209. [217]

    Advances in Neural Information Processing Systems , volume=

    Locally private online change point detection , author=. Advances in Neural Information Processing Systems , volume=

  210. [218]

    Journal of Machine Learning Research , volume=

    Fast online changepoint detection via functional pruning CUSUM statistics , author=. Journal of Machine Learning Research , volume=

  211. [219]

    Statistics and Computing , volume=

    A constant-per-iteration likelihood ratio test for online changepoint detection for exponential family models , author=. Statistics and Computing , volume=. 2024 , publisher=

    2024

  212. [220]

    Journal of the Royal Statistical Society Series B: Statistical Methodology , pages=

    Online multivariate changepoint detection: Leveraging links with computational geometry , author=. Journal of the Royal Statistical Society Series B: Statistical Methodology , pages=. 2025 , publisher=

    2025

  213. [221]

    NAR Genomics and Bioinformatics , volume=

    DiffSegR: An RNA-Seq data driven method for differential expression analysis using changepoint detection , author=. NAR Genomics and Bioinformatics , volume=. 2023 , publisher=

    2023

  214. [222]

    Bioinformatics , volume=

    Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning , author=. Bioinformatics , volume=. 2017 , publisher=

    2017

  215. [223]

    Nature biotechnology , volume=

    Integrative genomics viewer , author=. Nature biotechnology , volume=. 2011 , publisher=

    2011

  216. [224]

    The New England Journal of Statistics in Data Science , volume=

    E-detectors: A Nonparametric Framework for Sequential Change Detection , author=. The New England Journal of Statistics in Data Science , volume=. 2023 , publisher=

    2023

  217. [225]

    Genome biology , volume=

    Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive , author=. Genome biology , volume=. 2016 , publisher=

    2016

  218. [226]

    Fault tolerant flight control , pages=

    Fault detection and diagnosis for aeronautic and aerospace missions , author=. Fault tolerant flight control , pages=. 2010 , publisher=

    2010

  219. [227]

    2013 , school=

    Statistical Approaches for Segmentation: Application to Genome Annotation , author=. 2013 , school=

    2013

  220. [228]

    Nature biotechnology , volume=

    Standardized annotation of translated open reading frames , author=. Nature biotechnology , volume=. 2022 , publisher=

    2022

  221. [229]

    The Astrophysical Journal , volume=

    Gamma-ray burst detection with Poisson-FOCuS and other trigger algorithms , author=. The Astrophysical Journal , volume=. 2024 , publisher=

    2024

  222. [230]

    Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=

    Multiscale change point inference , author=. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=. 2014 , publisher=

    2014

  223. [231]

    arXiv preprint arXiv:2602.04322 , year=

    Exact Multiple Change-Point Detection Via Smallest Valid Partitioning , author=. arXiv preprint arXiv:2602.04322 , year=

  224. [232]

    Statistics and Computing , volume=

    A communication-efficient, online changepoint detection method for monitoring distributed sensor networks , author=. Statistics and Computing , volume=. 2024 , publisher=

    2024

  225. [233]

    The Annals of Statistics , pages=

    Using the generalized likelihood ratio statistic for sequential detection of a change-point , author=. The Annals of Statistics , pages=. 1995 , publisher=

    1995

  226. [234]

    The Astrophysical Journal , volume=

    The Fermi gamma-ray burst monitor , author=. The Astrophysical Journal , volume=. 2009 , publisher=

    2009

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.