On optimal segmentation and parameter tuning for multiple change-point detection and inference

References

• Pettitt AN. A non-parametric approach to the change point problem. Appl Stat. 1979;28(2):126–135.
   Google Scholar
• Aue A, Horváth L. Structural breaks in time series. J Time Ser Anal. 2013;34(1):1–16.
   Web of Science ®Google Scholar
• Inclan C, Tiao GC. Use of cumulative sums of squares for retrospective detection of changes of variance. J Am Stat Assoc. 1994;89(427):913–923.
   Web of Science ®Google Scholar
• Gao Z, Shang Z, Du P, et al. Variance change point detection under a smoothly-changing mean trend with application to liver procurement. J Am Stat Assoc. 2019;114(526):773–781.
   Web of Science ®Google Scholar
• Davis RA, Lee T, Rodriguez-Yam GA. Structural break estimation for nonstationary time series models. J Am Stat Assoc. 2006;101(473):223–239.
   Web of Science ®Google Scholar
• Gallagher C, Lund R, Robbins M. Changepoint detection in daily precipitation data. Environmetrics. 2012;23(5):407–419.
   Web of Science ®Google Scholar
• Dudding BP, Jennett W. Quality control charts: BS 600R. London: British Standards Institution; 1942.
   Google Scholar
• Jandhyala V, Fotopoulos S, MacNeill I, et al. Inference for single and multiple change-points in time series. J Time Ser Anal. 2013;34(4):423–446.
   Web of Science ®Google Scholar
• Wang D, Yu Y, Rinaldo A. Univariate mean change point detection: penalization, CUSUM and optimality. Electron J Stat. 2020;14(1):1917–1961.
   Web of Science ®Google Scholar
• Shi X. A survey of changepoint techniques for time series data [all dissertations]. Vol. 2697, Clemson University; 2020.
   Google Scholar
• Page ES. A test for a change in a parameter occurring at an unknown point. Biometrika. 1955;42(3/4):523–527.
   Web of Science ®Google Scholar
• MacNeill IB. Tests for change of parameter at unknown times and distributions of some related functionals on brownian motion. Ann Stat. 1974;2(5):950–962.
   Web of Science ®Google Scholar
• Lebarbier É. Detecting multiple change-points in the mean of Gaussian process by model selection. Signal Processing. 2005;85(4):717–736.
   Web of Science ®Google Scholar
• Lavielle M, Teyssiere G. Detection of multiple change-points in multivariate time series. Lithuanian Math J. 2006;46(3):287–306.
   Google Scholar
• Harchaoui Z, L e´vy-Leduc C. Multiple change-point estimation with a total variation penalty.J Am Stat Assoc. 2010;105(492):1480–1493.
   Web of Science ®Google Scholar
• Killick R, Fearnhead P, Eckley IA. Optimal detection of changepoints with a linear computational cost. J Am Stat Assoc. 2012;107(500):1590–1598.
   Web of Science ®Google Scholar
• Matteson DS, James NA. A nonparametric approach for multiple change point analysis of multivariate data. J Am Stat Assoc. 2014;109(505):334–345.
   Web of Science ®Google Scholar
• Fryzlewicz P. Wild binary segmentation for multiple change-point detection. Ann Stat. 2014;42(6):2243–2281.
   Web of Science ®Google Scholar
• Niu YS, Hao N, Zhang H. Multiple change-point detection: a selective overview. Stat Sci. 2016;31(4):611–623.
   Web of Science ®Google Scholar
• Haynes K, Eckley IA, Fearnhead P. Computationally effcient changepoint detection for a range of penalties. J Comput Graph Stat. 2017;26(1):134–143.
   Web of Science ®Google Scholar
• Baranowski RCY, Fryzlewicz P. Narrowest-over-threshold detection of multiple change points and change-point-like features. J R Stat Soc: Ser B. 2019;81(3):649–672.
   Google Scholar
• Li Y, Lund RB, Hewaarachchi A. Multiple changepoint detection with partial information on changepoint times. Electron J Stat. 2019;13(2):2462–2520.
   Web of Science ®Google Scholar
• Ray BK, Tsay RS. Bayesian methods for change-point detection long-range dependent processes. J Time Ser Anal. 2002;23(6):687–705.
   Web of Science ®Google Scholar
• Berkes I, Horváth L, Kokoszka PSQ-M. On discriminating between long-range dependence and changes in mean. Ann Stat. 2006;34(3):1140–1165.
   Web of Science ®Google Scholar
• Lu Q, Lund RB. Simple linear regression with multiple level shifts. Canadian J Stat. 2007;35(3):447–458.
   Web of Science ®Google Scholar
• Lund R, Wang XL, Lu Q, et al. Changepoint detection in periodic and autocorrelated time series. J Clim. 2007;20(20):5178–5190.
   Web of Science ®Google Scholar
• Gombay E. Change detection in autoregressive time series. J Multivar Anal. 2008;99(3):451–464.
   Web of Science ®Google Scholar
• Shao X, Zhang X. Testing for change points in time series. J Am Stat Assoc. 2010;105(491):1228–1240.
   Web of Science ®Google Scholar
• Robbins M, Gallagher C, Lund R, et al. Mean shift testing in correlated data. J Time Ser Anal. 2011;32(5):498–511.
   Web of Science ®Google Scholar
• Xie Y, Huang J, Willett R. Change-point detection for high-dimensional time series with missing data. IEEE J Sel Top Signal Process. 2012;7(1):12–27.
   Web of Science ®Google Scholar
• Shen J, Gallagher CM, Lu Q. Detection of multiple undocumented change-points using adaptive lasso. J Appl Stat. 2014;41(6):1161–1173.
   Web of Science ®Google Scholar
• Shao X. Self-normalization for time series: a review of recent developments. J Am Stat Assoc. 2015;110(512):1797–1817.
   Web of Science ®Google Scholar
• Cho H, Fryzlewicz P. Multiple-change-point detection for high dimensional time series via sparsified binary segmentation. J R Stat Soc B. 2015;77(2):475–507.
   Google Scholar
• Robbins MW, Gallagher CM, Lund RB. A general regression changepoint test for time series data. J Am Stat Assoc. 2016;111(514):670–683.
   Web of Science ®Google Scholar
• Chakar S, Lebarbier E, L e´vy-Leduc C, et al. A robust approach for estimating change-points in the mean of an AR(1) process. Bernoulli. 2017;23(2):1408–1447.
   Web of Science ®Google Scholar
• Zhang T, Lavitas L. Unsupervised self-normalized change-point testing for time series. J Am Stat Assoc. 2018;113(522):637–648.
   Web of Science ®Google Scholar
• Gurevich G, Vexler A. Retrospective change point detection: from parametric to distribution free policies. Commun Stat –Simul Comput. 2010;39(5):899–920.
   Web of Science ®Google Scholar
• Xie H, Li D, Xiong L. Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation. Stochastic Environ Res Risk Assess. 2014;28(7):1643–1655.
   Web of Science ®Google Scholar
• Ross GJ. Parametric and nonparametric sequential change detection in R: the cpm package.J Stat Softw. 2015;66(3):1–20.
   Web of Science ®Google Scholar
• Lavielle M. Detection of multiple changes in a sequence of dependent variables. Stoch Process Their Appl. 1999;83(1):79–102.
   Web of Science ®Google Scholar
• Tartakovsky A, Nikiforov I, Basseville M. Sequential analysis: hypothesis testing and changepoint detection. Boca Raton: Chapman and Hall/CRC; 2015.
   Google Scholar
• Auger IE, Lawrence CE. Algorithms for the optimal identification of segment neighborhoods. Bull Math Biol. 1989;51(1):39–54.
   PubMed Web of Science ®Google Scholar
• Jackson B, Scargle JD, Barnes D, et al. An algorithm for optimal partitioning of data on an interval. IEEE Signal Process Lett. 2005;12(2):105–108.
   Web of Science ®Google Scholar
• Rigaill G. A pruned dynamic programming algorithm to recover the best segmentations with 1 to Kmax change-points. Journal de la Société Française de Statistique. 2015;156(4):180–205.
   Google Scholar
• Maidstone R, Hocking T, Rigaill G, et al. On optimal multiple changepoint algorithms for large data. Stat Comput. 2017;27(2):519–533.
   PubMed Web of Science ®Google Scholar
• Fearnhead P, Rigaill G. Changepoint detection in the presence of outliers. J Am Stat Assoc. 2019;114(525):169–183.
   Web of Science ®Google Scholar
• Bellman R. The theory of dynamic programming. Bulletin Amer Math Soc. 1954;60:503–515.
   Web of Science ®Google Scholar
• Lavielle M, Moulines E. Least-squares estimation of an unknown number of shifts in a time series. J Time Ser Anal. 2000;21(1):33–59.
   Web of Science ®Google Scholar
• Picard F, Robin S, Lavielle MVC, et al. A statistical approach for array CGH data analysis. BMC Bioinformatics. 2005;6:27.
   PubMed Web of Science ®Google Scholar
• Lavielle M. Using penalized contrasts for the change-point problem. Signal Processing. 2005;85(8):1501–1510.
   Web of Science ®Google Scholar
• Niekum S, Osentoski SACG, Barto AG. CHAMP: changepoint detection using approximate model parameters. Carnegie-Mellon University Pittsburgh PA Robotics Institute; 2014. Tech Rep. (No. CMU-RITR-14–10) .
   Google Scholar
• Capizzi G, Masarotto G. Phase I distribution-free analysis of univariate data. J Quality Technol. 2013;45(3):273–284.
   Web of Science ®Google Scholar
• Sclove SL. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika. 1987;52:333–343.
   Web of Science ®Google Scholar
• Stine RA. Model selection using information theory and the MDL principle. Sociol Methods Res. 2004;33(2):230–260.
   Web of Science ®Google Scholar
• Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct Equ Modeling. 2007;14(4):535–569.
   Web of Science ®Google Scholar
• Tein JY, Coxe S, Cham H. Statistical power to detect the correct number of classes in latent profile analysis. Struct Equ Modeling. 2013;20(4):640–657.
   PubMed Web of Science ®Google Scholar
• Scott AJ, Knott M. A cluster analysis method for grouping means in the analysis of variance. Biometrics. 1974;30(3):507–512.
   Web of Science ®Google Scholar
• Killick R, Eckley IA. changepoint: an R package for changepoint analysis. J Stat Softw. 2014;58(3):1–19.
   Web of Science ®Google Scholar
• Texier G, Farouh M, Pellegrin L, et al. Outbreak definition by change point analysis: a tool for public health decision?. BMC Med Inform Decis Mak. 2016;16:33.
   PubMed Web of Science ®Google Scholar
• Serfling R. Methods for current statistical analysis of excess pneumonia-influenza deaths. Public Health Rep. 1963;78(6):494–506.
   PubMed Web of Science ®Google Scholar
• Parpoula C, Karagrigoriou A, Lambrou A. Epidemic intelligence statistical modelling for biosurveillance. In: Blömer J, Kotsireas IS, Kutsia T, Simos DE, editors. Mathematical aspects of computer and information sciences. Switzerland: Springer International Publishing; 2017. p. 349–363. (Lecture Notes in Computer Science (LNCS10963)).
   Google Scholar
• Kalligeris EN, Karagrigoriou A, Parpoula C. On mixed PARMA modeling of epidemiological time series data. Commun Stat: Case Stud, Data Anal Appl. 2020;6(1):36–49.
   Google Scholar
• Kalligeris EN, Karagrigoriou A, Parpoula C. Periodic-type ARMA modeling with covariates for time-series incidence data via changepoint detection. Stat Methods Med Res. 2020;29(6):1639–1649.
   PubMed Web of Science ®Google Scholar
• Parpoula C. A distribution-Free control charting technique based on change-point analysis for detection of epidemics. Stat Methods Med Res. 2022;1–18. DOI:10.1177/09622802221079347.
   Web of Science ®Google Scholar
• Pepe MS. The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press; 2003.
   Google Scholar
• DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–845.
   PubMed Web of Science ®Google Scholar
• Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–843.
   PubMed Web of Science ®Google Scholar
• Zhou XH, Obuchowski NA, McClish DK. Statistical methods in diagnostic medicine. 2nd ed. New Jersey: John Wiley and Sons; 2011.
   Google Scholar