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REVIEW 3 major objections 5 minor 44 references

DUST: A Duality-Based Pruning Method For Exact Multiple Change-Point Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read DUST reformulates change-point pruning as a dual evaluation with a provable no-gap guarantee.

desk verdict Promising duality-based pruning for change-point detection with a solid one-parameter closed form; the strong-duality claim for multivariate cases is not yet convincingly proved and key sections are missing. read the letter →

arxiv 2507.02467 v2 pith:RUFSHSFB submitted 2025-07-03 stat.ME stat.CO

Vincent Runge , Charles Truong , Simon Querné This is my paper · ORCID
classification stat.MEstat.CO
keywords multiplechange-pointdetectiondynamicprogrammingpruningdualitytheoryexponentialfamilyfunctionalPELTFPOP
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 tries to establish that a simple dual-function test can replace the two existing pruning strategies for exact multiple change-point detection. The DUST rule evaluates a Lagrangian dual of a constrained optimization problem and prunes a candidate change point whenever the dual exceeds a fixed threshold; the authors prove that this is safe because, for $d$-parameter exponential-family costs with at most $d$ constraints, the dual has no gap to the constrained optimum. If the proof holds, the practical payoff is a single algorithm that is as simple as PELT, as efficient as FPOP in sparse-change regimes, and applicable to multivariate and non-Gaussian models where FPOP's functional geometry breaks down.

What carries the argument

The load-bearing object is the decision function $D_{st}(x) = -D^{*}(\sigma(x)) - \phi(x)$, where $\sigma(x) = S_{st} + \sum_{r} x_r \Delta S_{rst}$, $\phi(x) = Q_{st} + \sum_{r} x_r \Delta Q_{rst}$, and $D^{*}$ is the strictly convex function $D^{*}(x) = x \cdot (\nabla A)^{-1}(x) - A((\nabla A)^{-1}(x))$ built from the log-partition $A$. It is a renormalized Lagrangian dual of the pruning problem; the DUST test is simply $D_{st}(x_0) > 0$ at some feasible point. The exactness guarantee is carried by Theorem 8's strong-duality result, whose geometric proof shows that the epigraph of the objective over the constraint surface is convex when the dual uses $q \le d$ constraints, and that this convexity fails with $d+1$ constraints.

What would settle it

Enumerate or randomly generate small datasets for one exponential-family model, compute the exact optimal-partitioning cost, and run DUST with $q \le d$ constraints. Any run whose returned segmentation has a higher penalized cost than the exact optimum, or any generated problem with $\max_{\mu\in\Omega_\mu} D_{st}(\mu) < R_s^t$ while the constrained minimum is finite, would disprove the no-duality-gap claim. A focused version is the paper's own three-point Gaussian example: with one constraint the primal and dual values should coincide, and with two constraints they should not.

Watch

Extended reading notes

Core claim

The central claim is Theorem 8: when the dual function $D_{st}$ is built with at most $d$ constraints for a $d$-parametric exponential-family cost, there is no duality gap, meaning $\min_{\theta\in\Theta_s^t(R)} q_s^t(\theta) = \max_{\mu\in\Omega_\mu} D_{st}(\mu)$. This is what makes the DUST rule safe: for any feasible point $x_0$, if the decision function $D_{st}(x_0) = -D^*(\sigma(x_0)) - \phi(x_0)$ is positive, then the constrained minimum exceeds $Q_t+\beta$, so index $s$ can never serve as the last change point in an exact solution. The paper also derives a closed-form maximum of the decision function for one-parameter costs and for the Gaussian change-in-mean-and-variance problem, and it reports simulations in which DUST leaves fewer than $n^{0.15}$ candidate indices and runs faster than FPOP on large non-Gaussian data.

Load-bearing premise

The load-bearing premise is that the geometric proof of Theorem 8 holds: the statistic points $S_{r_i s}$ are in general position and the path $h_-$ used to build the convex epigraph is differentiable with convex coordinate $x(\alpha)$; if that fails, the dual maximum could lie below the constrained minimum and DUST's pruning would not be provably exact.

Editorial extensions

If this is right

  • Pruning with the DUST test is safe: an index $s$ is discarded only if the decision function is positive, which implies $R_s^t > Q_t + \beta$, so the dynamic program still returns the exact optimal segmentation.
  • For one-parameter cost functions the decision function has a closed-form maximum, turning DUST into an inequality test comparable in simplicity to PELT while pruning in sparse-change regimes where PELT barely prunes.
  • For $d$-parameter models the test remains provably exact with up to $d$ constraints, so multivariate and non-Gaussian exponential-family models can be segmented exactly under aggressive pruning.
  • Empirical results show the number of surviving candidate indices grows like $n^\alpha$ with $\alpha < 0.15$, giving near-linear practical running time, with DUST overtaking FPOP on Gaussian data by roughly length $5{,}000$ and beating it immediately on Poisson data.
  • On a mouse-monitoring force-platform dataset under a change-in-variance model, DUST segments about $400{,}000$ samples in seconds and recovers the expected nighttime activity pattern.

Reading between the lines

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

  • A natural extension the paper leaves open is choosing the constraint set $R$ adaptively or evaluating the dual at several points; Theorem 8 suggests the lower bound can only increase, but no selection rule is proven optimal.
  • Because PELT is exactly the dual evaluated at zero, DUST forms a whole family of pruning tests interpolating between PELT and the maximal dual; proving worst-case complexity bounds for the maximized test is the obvious next target.
  • The unified decision function $G_D(z)$ of Equation (19) hints that all indices could be tested by one global optimization over $z$; if cheap maximization became available, pruning could be stronger still, but that is future work.
  • The no-duality-gap structure is not tied to time series: any additive-cost optimization with exponential-family-like convexity could use the same dual test, for example graph-constrained segmentation or non-linear penalties, but the geometric hypotheses would need re-checking.
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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 proposes DUST, a pruning rule for exact multiple change-point detection in exponential-family models. The authors reformulate the pruning task as a constrained minimization of the segment cost q_s_t over the region where q_s_t is minimal, derive a Lagrangian dual and an equivalent decision function, and prune an index s whenever the dual evaluated at a chosen point exceeds Q_t + beta. For one-parameter costs they give a closed-form maximum of the decision function; for q <= d constraints they claim a strong-duality theorem. The paper reports univariate simulation studies across several exponential-family models and an application to mouse monitoring data under a change-in-variance model.

Significance. If the strong-duality theorem is valid, DUST is a genuinely useful contribution: it generalizes PELT (which corresponds to evaluating the dual at zero), supplies a simple closed-form pruning test for one-parameter models, and shows substantial speedups over FPOP for non-Gaussian models. The weak-duality safety of the pruning rule is solid and is a strength of the paper: for any admissible mu0, D(mu0) <= R_s_t, so D(mu0) > Q_t + beta implies R_s_t > Q_t + beta, and Proposition 3 guarantees exactness. Theorem 8 is not needed for that safety property, but it is load-bearing for the paper's advertised no-duality-gap result and for the claimed efficiency of the multivariate extension. The current proof of Theorem 8 has unstated geometric hypotheses, and the multivariate simulation section is a placeholder; both issues need to be resolved before the claims in the abstract can be accepted.

major comments (3)
  1. [Appendix D.2, Theorem 8 and Lemmas 3-4] The proof of strong duality relies on unstated geometric and general-position assumptions. Lemma 3 assumes that the points S_ris are in general position without defining this condition, and it asserts that a straight line intersects a level curve of q0 - q1 in two points; the existence and multiplicity of such intersections are not proved. Lemma 4 concludes convexity of the projection from the statement that 'f(theta, alpha) <= 0 or f(theta, alpha) >= 0 is convex and bounded', which is not a valid argument for the claimed intersection property. The theorem proof also assumes without justification that the selected branch h- is differentiable at 0 and that alpha -> x(alpha) is convex; the sign argument uses the inequality (nabla A(h(alpha)) - S_s1s) . u < 0 and excludes the nullity case as a boundary phenomenon without proof. These hypotheses must be stated precisely and proved, or replaced by a different argument; otherwise Theorem 8 is not established.
  2. [Section 4.2, Theorem 8 statement] For q < d, the paragraph preceding Theorem 8 says that one can 'add d - q constraints of type q_s_t(theta) - q_r_t(theta) <= 0 that we know would be unused (no equality at the optimum point)' to reach d constraints. This presumes the existence of d - q candidate indices whose constraints are inactive at the optimum and whose addition does not change the feasible set Theta_s_t(R); no such guarantee is given, and if fewer than d - q candidate indices are available the proof cannot proceed. The theorem should either be stated only for q = d, or a separate argument for q < d must be supplied.
  3. [Section 5.3 and Appendix G] The manuscript states that Section 5.3 'will be updated post-publication' and that Appendix G is 'To be determined', yet the abstract and Introduction claim that DUST is 'broadly applicable to parametric models of any dimension' and 'highly flexible'. The current version contains no simulation study of multivariate signals; the only evidence beyond the univariate setting is the two-parameter mean-and-variance example in Section 3.4. Either include the multivariate simulation results before publication or restrict the claims to the univariate and one-parameter settings that are actually supported.
minor comments (5)
  1. [Section 3.4] The phrase '0 indice is pruned' should read '0 indices are pruned'.
  2. [Sections 3.1-3.2] The symbol D is used for both the dual function (Proposition 4) and the decision function (Proposition 5), and D* also appears; this is confusing and should be resolved by renaming one of these quantities.
  3. [Proposition 4 and Table 3] Proposition 4 says the dual domain is bounded by mu_max <= 1, while Table 3 lists x_max = +infinity for several distributions. Since x = mu/(1-mu), mu_max = 1 corresponds to x_max = infinity; the distinction between the mu-domain and the x-domain should be made explicit whenever these values are compared.
  4. [Section 5.1.1] The list of models uses the abbreviation G for both Gauss and Geometric, which is confusing; use distinct abbreviations.
  5. [Appendix B.1, proof of Proposition 1] The proof contains apparent typos in the index ranges (for example, 'For s in {t+1,...,t+T}, we have for index s < t' should likely refer to s > t), and the chain of inequalities is difficult to follow; please revise.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the DUST pruning rule is derived from, but not assumed by, the same objective it prunes; no fitted parameter is renamed as a prediction.

full rationale

The central claim of the paper is that the dual-based test Dst(x0) > 0 is a safe pruning rule, backed by weak duality (D <= R) and, when q <= d, by the strong-duality statement of Theorem 8. The decision function Dst(x) = -D*(sigma(x)) - phi(x) (Eq. 17) is explicitly constructed from the Lagrangian of the pruning problem (11), not assumed to equal its optimum: Theorem 8 is a result to be proved, and the equality R = max D is not definitional. The relationship to PELT is also presented as an extension, not as a renamed result: Remark 1 states that PELT evaluates the dual at zero while DUST samples mu0 in [0, mu_max). The only fitted values in the paper are the simulation penalty scale factors calibrated on length-10^3 no-change data; these affect benchmark conditions and do not enter the exactness or duality proofs. The proof of Theorem 8 does contain unstated geometric hypotheses (Appendix D.2 invokes general position of the points Sris, differentiability of h- at 0, and convexity of alpha -> x(alpha) without precise verification), and Sections 5.3 and G explicitly say they will be updated post-publication. These are rigor and completeness concerns, not circularity: because the pruning rule only requires weak duality, a failure of strong duality would weaken the efficiency guarantee but would not make the pruning test assume its own conclusion. The self-citations present in the paper are used as background, competitor algorithms, or application data sources, and none carries the load of the paper's main duality theorem.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The algorithm introduces no new physical or probabilistic entity; it defines a mathematical dual decision function. The main external inputs are the exponential-family model (standard), the user-specified penalty beta, and the calibration of penalty scale factors in simulations.

free parameters (2)
  • per-model penalty scale factor = Gauss 1, Poisson 2/3, Exponential 3/4, Geometric 2/3, Bernoulli 2/3, Binomial 1/6, Negative Binomial 1/10, Variance 1
    Calibrated in Section 5.1.2 as the smallest penalty value yielding no detected change on length-1000 no-change data, then used to make models comparable. This is fitting to simulation data and affects the comparison, not the mathematical derivation of DUST.
  • dual evaluation point mu0 and index selection rule pr = Dirac at maximum, random, zero, or quasi-Newton; deterministic largest-below index
    Algorithm 1 leaves these choices to the user. Efficiency depends on them, but any choice in the stated domain is safe for exactness. They are not fitted to data in the central derivation.
assumptions (6)
  • domain assumption Canonical exponential family with minimal representation, strictly convex log-partition A, and natural parameter domain Theta
    Section 2.1, Eq. (1). All segment costs and dual functions are built on this representation.
  • standard math Segment cost additivity c(y_at; theta) - c(y_bt; theta) = c(y_ab; theta)
    Eq. (2). This property makes pruning dominance persist to later times, the foundation of the pruning rules.
  • standard math Weak duality: for any mu in the dual domain, D(mu) is a lower bound on the constrained minimum
    Used in Proposition 4 and Section 4. This is standard Lagrangian relaxation and makes any positive dual test safe.
  • ad hoc to paper Strong duality geometric convexity, including general position of the sufficient statistic increments S_ris and existence of the differentiable path h-
    Appendix D.2, Lemma 3 and the proof of Theorem 8. The no-duality-gap result with q <= d constraints depends on these unstated geometric conditions, which are not standard Slater-type conditions.
  • domain assumption Independent observations within and between segments
    Section 2.1 likelihood factorization. The cost function and dynamic programming recursion rely on independence.
  • ad hoc to paper Pruning constraints are considered only for r < s; the r > s case is described as inefficient and excluded
    Section 3.1 and Remark 2. The closed-form maximum and domain bounds are derived under this ordering.

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Cite this review

Pith. "Pith review of DUST: A Duality-Based Pruning Method For Exact Multiple Change-Point Detection." pith.science (2026). https://pith.science/paper/RUFSHSFB

@misc{pith2026250702467,
  author       = {Pith},
  title        = {Pith review of: DUST: A Duality-Based Pruning Method For Exact Multiple Change-Point Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RUFSHSFB}},
  note         = {Machine review of arXiv:2507.02467}
}
read the original abstract

We tackle the challenge of detecting multiple change points in large time series by optimising a penalised likelihood derived from exponential family models. Dynamic programming algorithms can solve this task exactly with at most quadratic time complexity. In recent years, the development of pruning strategies has drastically improved their computational efficiency. However, the two existing approaches have notable limitations: PELT struggles with pruning efficiency in sparse-change scenarios, while FPOP's structure is not adapted to multi-parametric settings. To address these issues, we introduce the DUal Simple Test (DUST) framework, which prunes candidate changes by evaluating a dual function against a threshold. This approach is highly flexible and broadly applicable to parametric models of any dimension. Under mild assumptions, we establish strong duality for the underlying non-convex pruning problem. We demonstrate DUST's effectiveness across various change-point regimes and models. In particular, for one-parametric models, DUST matches the simplicity of PELT with the efficiency of FPOP. Its use is especially advantageous for non-Gaussian models. Finally, we apply DUST to mouse monitoring time series under a change-in-variance model, illustrating its ability to recover the optimal change-point structure efficiently.

Figures

Figures reproduced from arXiv: 2507.02467 by the authors.

Figure 1
Figure 1. For data with no change, the number of indices saved by DUST over time is [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the dual maximum value ( [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Median number of remaining candidate indices at each time for [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Log-log comparison of number of remaining indices over times as a function of data [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]
Figure 5
Figure 5. Figure 5: Log-log comparison of execution times between DUST and FPOP as a function [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]
Figure 6
Figure 6. Figure 6: Log-log comparison of execution times for varying numbers of change points in [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]
Figure 7
Figure 7. Figure 7: Median number of candidate indices upon exit of the DUST algorithm for [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]
Figure 8
Figure 8. Figure 8: For Mouse ColQ, the activity measured by a force platform. The x-axis is the time [PITH_FULL_IMAGE:figures/full_fig_p024_8.png]
Figure 9
Figure 9. Figure 9: Poisson functional cost Q20(·) obtained from 20 data points. We identify easily the 20 different inner functions from which the functional cost is built. We choose Y = 30 and β = 0.995Y log(n/(n − 1)). Due to the integer approximation for y˜t , only 14 inner functions …
Figure 10
Figure 10. Figure 10: Examples with 30 data points in worst case complexity (no pruning) with Gaus￾sian model (middle curve in black) and two examples with exponential model with two different penalty values (beta). We chose Y = 1. C Proofs of Section 3 C.1 Proof of Proposition 4 The prima…

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Reference graph

Works this paper leans on

44 extracted references · 41 canonical work pages

  1. [1]

    A survey of methods for time series change point detection.Knowledge and information systems, 51(2):339–367, 2017

    Samaneh Aminikhanghahi and Diane J Cook. A survey of methods for time series change point detection.Knowledge and information systems, 51(2):339–367, 2017

    work page 2017

  2. [2]

    Algorithms for the optimal identification of segment neighborhoods.Bulletin of mathematical biology, 51(1):39–54, 1989

    Ivan E Auger and Charles E Lawrence. Algorithms for the optimal identification of segment neighborhoods.Bulletin of mathematical biology, 51(1):39–54, 1989

    work page 1989

  3. [3]

    Computation and analysis of multiple structural change models

    Jushan Bai and Pierre Perron. Computation and analysis of multiple structural change models. Journal of applied econometrics, 18(1):1–22, 2003

    work page 2003

  4. [4]

    Change-point analysis in financial networks

    Sayantan Banerjee and Kousik Guhathakurta. Change-point analysis in financial networks. Stat, 9(1):e269, 2020

    2020

  5. [5]

    Springer Science & Business Media, 2013

    Emily Brodsky and Boris S Darkhovsky.Nonparametric methods in change point problems, volume 243. Springer Science & Business Media, 2013

    work page 2013

  6. [6]

    Fundamentals of statistical exponential families: with appli- cations in statistical decision theory

    Lawrence D Brown. Fundamentals of statistical exponential families: with appli- cations in statistical decision theory. In Fundamentals of statistical exponential families: with applications in statistical decision theory. Ims, 1986

    work page 1986

  7. [7]

    A fast two-stage dy- namic programming algorithm for change-points model with application in speech signal

    Kuo-Ching Chang, Chui-Liang Chiang, and Chung-Bow Lee. A fast two-stage dy- namic programming algorithm for change-points model with application in speech signal. In 2010 Fourth international conference on genetic and evolutionary com- puting, pages 366–370. IEEE, 2010

    work page 2010

  8. [8]

    Parametric statistical change point analysis, volume 192

    Jie Chen, Arjun K Gupta, and AK Gupta. Parametric statistical change point analysis, volume 192. Springer, 2000

    work page 2000

Show all 44 references
  1. [9]

    Model selection for the segmentation of multiparameter exponential family distributions.Electronic Journal of Statistics, 11(1):800 – 842, 2017

    Alice Cleynen and Émilie Lebarbier. Model selection for the segmentation of multiparameter exponential family distributions.Electronic Journal of Statistics, 11(1):800 – 842, 2017

    2017

  2. [10]

    P. M. Rodríguez Cruz, J. Cossins, D. Beeson, and A. Vincent. The neuromus- cular junction in health and disease: Molecular mechanisms governing synaptic formation and homeostasis.Frontiers in Molecular Neuroscience, 13, 12 2020

    2020

  3. [11]

    Limit theorems in change-point analysis.(No Title), 1997

    Miklós Csörgö and Lajos Horváth. Limit theorems in change-point analysis.(No Title), 1997

    1997

  4. [12]

    Detecting changes in slope with an l 0 penalty

    Paul Fearnhead, Robert Maidstone, and Adam Letchford. Detecting changes in slope with an l 0 penalty. Journal of Computational and Graphical Statistics, 28(2):265–275, 2019. 42

    2019

  5. [13]

    Fast change point detection for electricity market analysis

    William Gu, Jaesik Choi, Ming Gu, Horst Simon, and Kesheng Wu. Fast change point detection for electricity market analysis. In2013 IEEE International Con- ference on Big Data, pages 50–57. IEEE, 2013

    2013

  6. [14]

    A regularized kernel-based approach to unsupervised audio segmentation

    Zaid Harchaoui, Félicien Vallet, Alexandre Lung-Yut-Fong, and Olivier Cappé. A regularized kernel-based approach to unsupervised audio segmentation. In2009 IEEE international conference on acoustics, speech and signal processing, pages 1665–1668. IEEE, 2009

    2009

  7. [15]

    Constrained dynamic programming and supervised penalty learning algorithms for peak detection in genomic data.Journal of Machine Learning Research, 21(87):1– 40, 2020

    Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, and Guillaume Bourque. Constrained dynamic programming and supervised penalty learning algorithms for peak detection in genomic data.Journal of Machine Learning Research, 21(87):1– 40, 2020

    2020

  8. [16]

    An algorithm for optimal partitioning of data on an interval.IEEE Signal Processing Letters, 12(2):105–108, 2005

    Brad Jackson, Jeffrey D Scargle, David Barnes, Sundararajan Arabhi, Alina Alt, Peter Gioumousis, Elyus Gwin, Paungkaew Sangtrakulcharoen, Linda Tan, and Tun Tao Tsai. An algorithm for optimal partitioning of data on an interval.IEEE Signal Processing Letters, 12(2):105–108, 2005

    2005

  9. [17]

    Infer- ence for single and multiple change-points in time series.Journal of Time Series Analysis, 34(4):423–446, 2013

    Venkata Jandhyala, Stergios Fotopoulos, Ian MacNeill, and Pengyu Liu. Infer- ence for single and multiple change-points in time series.Journal of Time Series Analysis, 34(4):423–446, 2013

    2013

  10. [18]

    Fast nonconvex deconvolution of calcium imaging data.Biostatistics, 21(4):709– 726, 2020

    Sean W Jewell, Toby Dylan Hocking, Paul Fearnhead, and Daniela M Witten. Fast nonconvex deconvolution of calcium imaging data.Biostatistics, 21(4):709– 726, 2020

    2020

  11. [19]

    Efficient change-points detection for genomic sequences via cumulative segmented regression.Bioinformatics, 38(2):311–317, 2022

    Shengji Jia and Lei Shi. Efficient change-points detection for genomic sequences via cumulative segmented regression.Bioinformatics, 38(2):311–317, 2022

    2022

  12. [20]

    Optimal detection of change- points with a linear computational cost.Journal of the American Statistical As- sociation, 107(500):1590–1598, 2012

    Rebecca Killick, Paul Fearnhead, and Idris A Eckley. Optimal detection of change- points with a linear computational cost.Journal of the American Statistical As- sociation, 107(500):1590–1598, 2012

    2012

  13. [21]

    Krejci, P

    E. Krejci, P. Konstantin, O. Lenina, V. Bernard, T. Germain, C. Truong, L. Nu- rullin, G. Sibgatullina, D. Samigullin, and K. Ohno. Anα7 nicotinic and gabab receptor-mediated pathway controls acetylcholine release in the tripartite neuro- muscular junction.The Journal of Physi...

    2024

  14. [22]

    Diffsegr: an rna-seq data driven method for differential expression analysis using changepoint detection

    Arnaud Liehrmann, Etienne Delannoy, Alexandra Launay-Avon, Elodie Gilbault, Olivier Loudet, Benoît Castandet, and Guillem Rigaill. Diffsegr: an rna-seq data driven method for differential expression analysis using changepoint detection. NAR Genomics and Bioinformatics, 5(4):lq...

    2023

  15. [23]

    On op- timal multiple changepoint algorithms for large data.Statistics and computing, 27:519–533, 2017

    Robert Maidstone, Toby Hocking, Guillem Rigaill, and Paul Fearnhead. On op- timal multiple changepoint algorithms for large data.Statistics and computing, 27:519–533, 2017

    2017

  16. [24]

    A test for a change in a parameter occurring at an unknown point

    ES Page. A test for a change in a parameter occurring at an unknown point. Biometrika, 42(3/4):523–527, 1955. 43

    1955

  17. [25]

    Continuous inspection schemes.Biometrika, 41(1/2):100–115, 1954

    Ewan S Page. Continuous inspection schemes.Biometrika, 41(1/2):100–115, 1954

    1954

  18. [26]

    Geometric-basedprun- ing rules for change point detection in multiple independent time series.Computo, 2024

    LiudmilaPishchagina, GuillemRigaill, andVincentRunge. Geometric-basedprun- ing rules for change point detection in multiple independent time series.Computo, 2024

    2024

  19. [27]

    Online multivariate changepoint detection: Leveraging links with computational geometry.arXiv preprint arXiv:2311.01174, 2023

    Liudmila Pishchagina, Gaetano Romano, Paul Fearnhead, Vincent Runge, and Guillem Rigaill. Online multivariate changepoint detection: Leveraging links with computational geometry.arXiv preprint arXiv:2311.01174, 2023

    2023 arXiv

  20. [28]

    A review and comparison of changepoint detection techniques for climate data.Journal of applied meteorology and climatology, 46(6):900–915, 2007

    Jaxk Reeves, Jien Chen, Xiaolan L Wang, Robert Lund, and Qi Qi Lu. A review and comparison of changepoint detection techniques for climate data.Journal of applied meteorology and climatology, 46(6):900–915, 2007

    2007

  21. [29]

    A pruned dynamic programming algorithm to recover the best segmentations with 1 to k_{max} change-points

    Guillem Rigaill. A pruned dynamic programming algorithm to recover the best segmentations with 1 to k_{max} change-points. Journal de la société française de statistique, 156(4):180–205, 2015

    2015

  22. [30]

    Fast online changepoint detection via functional pruning cusum statistics.Journal of Machine Learning Research, 24(81):1–36, 2023

    Gaetano Romano, Idris A Eckley, Paul Fearnhead, and Guillem Rigaill. Fast online changepoint detection via functional pruning cusum statistics.Journal of Machine Learning Research, 24(81):1–36, 2023

    2023

  23. [31]

    Detect- ing abrupt changes in the presence of local fluctuations and autocorrelated noise

    Gaetano Romano, Guillem Rigaill, Vincent Runge, and Paul Fearnhead. Detect- ing abrupt changes in the presence of local fluctuations and autocorrelated noise. Journal of the American Statistical Association, 117(540):2147–2162, 2022

    2022

  24. [32]

    Is a finite intersection of balls covered by a finite union of balls in euclidean spaces? Journal of Optimization Theory and Applications, 187(2):431– 447, 2020

    Vincent Runge. Is a finite intersection of balls covered by a finite union of balls in euclidean spaces? Journal of Optimization Theory and Applications, 187(2):431– 447, 2020

    2020

  25. [33]

    gfpop: An r package for univariate graph- constrained change-point detection

    Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, and Guillem Rigaill. gfpop: An r package for univariate graph- constrained change-point detection. Journal of Statistical Software, 106(6):1–39, 2023

    2023

  26. [34]

    A cluster analysis method for grouping means in the analysis of variance.Biometrics, pages 507–512, 1974

    Andrew Jhon Scott and Martin Knott. A cluster analysis method for grouping means in the analysis of variance.Biometrics, pages 507–512, 1974

    1974

  27. [35]

    On tests for detecting change in mean.The Annals of statistics, pages 98–108, 1975

    Ashish Sen and Muni S Srivastava. On tests for detecting change in mean.The Annals of statistics, pages 98–108, 1975

    1975

  28. [36]

    Change-point problems ims lecture notes- monograph series (volume 23, 1994) a nonparametric test for homogeneity: Ap- plications to parameter estimation by k

    IMS Lecture Notes-Monograph Series. Change-point problems ims lecture notes- monograph series (volume 23, 1994) a nonparametric test for homogeneity: Ap- plications to parameter estimation by k. ghoudi and d. mcdonald.Change-point Problems, 23:149, 1994

    1994

  29. [37]

    Changepoint detection: An analysis of the central england temperature series.Journal of Cli- mate, 35(19):6329–6342, 2022

    Xueheng Shi, Claudie Beaulieu, Rebecca Killick, and Robert Lund. Changepoint detection: An analysis of the central england temperature series.Journal of Cli- mate, 35(19):6329–6342, 2022

    2022

  30. [38]

    Selective review of offline change point detection methods.Signal Processing, 167:107299, 2020

    Charles Truong, Laurent Oudre, and Nicolas Vayatis. Selective review of offline change point detection methods.Signal Processing, 167:107299, 2020. 44

    2020

  31. [39]

    Optimal change-point detection and localization., 2020

    Nicolas Verzelen, Magalie Fromont, Matthieu Lerasle, and Patricia Reynaud- Bouret. Optimal change-point detection and localization., 2020

    2020

  32. [40]

    L. Yu. Vostrikova. Detecting ”disorder” in multidimensional random processes. Sov. Math., Dokl., 24:55–59, 1981

    1981

  33. [41]

    Graphical models, exponential fam- ilies, and variational inference.Foundations and Trends® in Machine Learning, 1(1–2):1–305, 2008

    Martin J Wainwright, Michael I Jordan, et al. Graphical models, exponential fam- ilies, and variational inference.Foundations and Trends® in Machine Learning, 1(1–2):1–305, 2008

    2008

  34. [42]

    Estimating the number of change-points via schwarz’criterion

    Yi-Ching Yao. Estimating the number of change-points via schwarz’criterion. Statistics & Probability Letters, 6(3):181–189, 1988

    1988

  35. [43]

    A modified bayes information criterion with applications to the analysis of comparative genomic hybridization data.Biomet- rics, 63:22–32, 04 2007

    Nancy Zhang and David Siegmund. A modified bayes information criterion with applications to the analysis of comparative genomic hybridization data.Biomet- rics, 63:22–32, 04 2007

    2007

  36. [44]

    Asymptotic distribution-free change-point detection for modern data based on a new ranking scheme.arXiv preprint arXiv:2206.03038, 2022

    Doudou Zhou and Hao Chen. Asymptotic distribution-free change-point detection for modern data based on a new ranking scheme.arXiv preprint arXiv:2206.03038, 2022. 45

    2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.