7,029
Views
304
CrossRef citations to date
0
Altmetric
Theory and Methods

A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data

Pages 334-345 | Received 07 Nov 2012, Published online: 19 Mar 2014

REFERENCES

  • Akoglu, L., and Faloutsos, C. (2010), “Event Detection in Time Series of Mobile Communication Graphs,” in Proceedings of the Army Science Conference, pp. 18–25.
  • Arlot, S., Celisse, A., Harchaoui, Z. (2012), Kernel Change-Point Detection, arXiv:1202.3878. Available at http://adsabs.harvard.edu/abs/2012arXiv1202.3878A
  • Bleakley, K., and Vert, J.-P. (2011), “The Group Fused Lasso for Multiple Change-Point Detection,”. Technical Report HAL-00602121, Bioinformatics Center (CBIO): Author.
  • Bolton, R., Hand, D. (2002), Statistical Fraud Detection: A Review, Statistical Science, 17, 235–255.
  • Carlin, B.P., Gelfand, A.E., Smith, A.F. (1992), Hierarchical Bayesian Analysis of Changepoint Problems, Applied Statistics, 41, 389–405.
  • Cho, H., Fryzlewicz, P. (2012), Multiscale and Multilevel Technique for Consistent Segmentation of Nonstationary Time Series, Statistica Sinica, 22.
  • Davis, R., Lee, T., Rodriguez-Yam, G. (2006), Structural Break Estimation for Nonstationary Time Series Models, Journal of the American Statistical Association, 101, 223–239.
  • Fowlkes, E.B., Mallows, C.L. (1983), A Method for Comparing Two Hierarchical Clusterings, Journal of the American Statistical Association, 78, 553–569.
  • Gandy, A. (2009), Sequential Implementation of Monte Carlo Tests With Uniformly Bounded Resampling Risk, Journal of the American Statistical Association, 104, 1504–1511.
  • Guralnik, V., Srivastava, J. (1999), Event Detection From Time Series Data, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’99. ACM.
  • Harchaoui, Z., Cappe, O. (2007), Retrospective Multiple Change-Point Estimation With Kernels, Statistical Signal Processing, 2007. SSP ’07. IEEE/SP 14th Workshop, pp. 768–772.
  • Hariz, S.B., Wylie, J.J., Zhang, Q. (2007), Optimal Rate of Convergence for Nonparametric Change-Point Estimators for Nonstationary Sequences, The Annals of Statistics, 35, 1802–1826.
  • Hawkins, D.M. (2001), Fitting Multiple Change-Point Models to Data, Computational Statistics and Data Analysis, 37323–341.
  • Hoeffding, W. (1961), “The Strong Law of Large Numbers for U-statistics,”. Technical Report 302, North Carolina State University, Department of Statistics.
  • Hubert, L., Arabie, P. (1985), Comparing Partitions, Journal of Classification, 2, 193–218.
  • James, N.A., Matteson, D.S. (2013), ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data, arXiv:1309.3295. Available at http://adsabs.harvard.edu/abs/2013arXiv1309.3295J
  • Johnson, O., Sejdinovic, D., Cruise, J., Ganesh, A., Piechocki, R. (2011), Non-Parametric Change-Point Detection Using String Matching Algorithms, arXiv:1106.5714. Available at http://adsabs.harvard.edu/abs/2011arXiv1106.5714J
  • Kawahara, Y., Sugiyama, M. (2011), Sequential Change-Point Detection Based on Direct Density-Ratio Estimation, Statistical Analysis and Data Mining, 5, 114–127.
  • Killick, R., Fearnhead, P., Eckley, I. (2012), Optimal Detection of Changepoints With a Linear Computational Cost, Journal of the American Statistical Association, 107, 1590–1598.
  • Kim, A., Marzban, C., Percival, D., Stuetzie, W. (2009), Using Labeled Data to Evaluate Change Detectors in a Multivariate Streaming Environment, Signal Processing, 89, 2529–2536.
  • Lavielle, M., Teyssière, G. (2006), Detection of Multiple Change-Points in Multivariate Time Series, Lithuanian Mathematical Journal, 46, 287–306.
  • Lung-Yut-Fong, A., Lévy-Leduc, C., Cappé, O. (2011), Homogeneity and Change-Point Detection Tests for Multivariate Data Using Rank Statistics, arXiv:1107.1971. Available at http://adsabs.harvard.edu/abs/2011arXiv1107.1971L
  • Mampaey, M., Vreeken, J. (2011), Summarizing Categorical Data by Clustering Attributes, Data Mining and Knowledge Discovery, 24, 1–44.
  • Matteson, D.S., McLean, M.W., Woodard, D.B., Henderson, S.G. (2011), Forecasting Emergency Medical Service Call Arrival Rates, The Annals of Applied Statistics, 5, 1379–1406.
  • Morey, L.C., Agresti, A. (1984), The Measurement of Classification Agreement: An Adjustment to the Rand Statistic for Chance Agreement, Educational and Psychological Measurement, 44, 33–37.
  • Muggeo, V.M., Adelfio, G. (2011), Efficient Change Point Detection for Genomic Sequences of Continuous Measurements, Bioinformatics, 27, 161–166.
  • Olshen, A.B., Venkatraman, E. (2004), Circular Binary Segmentation for the Analysis of Array-Based DNA Copy Number Data, Biostatistics, 5, 557–572.
  • Page, E. (1954), Continuous Inspection Schemes, Biometrika, 41, 100–115.
  • R Development Core Team., (2012), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
  • Rand, W.M. (1971), Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, 66, 846–850.
  • Rigaill, G. (2010), Pruned Dynamic Programming for Optimal Multiple Change-Point Detection, arXiv:1004.0887. Available at http://adsabs.harvard.edu/abs/2010arXiv1004.0887R
  • Rizzo, M.L., Székely, G.J. (2010), Disco Analysis: A Nonparametric Extension of Analysis of Variance, The Annals of Applied Statistics, 4, 1034–1055.
  • Sequeira, K., Zaki, M. (2002), ADMIT: Anomaly-based Data Mining for Intrusions, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02. ACM.
  • Székely, G.J., Rizzo, M.L. (2005), Hierarchical Clustering via Joint Between-Within Distances: Extending Ward’s Minimum Variance Method, Journal of Classification, 22, 151–183.
  • Talih, M., Hengartner, N. (2005), Structural Learning With Time-Varying Components: Tracking the Cross-Section of Financial Time Series, Journal of the Royal Statistical Society, 67, 321–341.
  • Venkatraman, E. (1992), Consistency Results in Multiple Change-Point Problems, Ph.D. Thesis, Stanford University.
  • Vostrikova, L. (1981), Detection Disorder in Multidimensional Random Processes, Soviet Mathematics Doklady, 24, 55–59.
  • Yao, Y.C. (1987), Estimating the Number of Change-Points via Schwarz Criterion, Statistics & Probability Letters, 6, 181–189.
  • Zhang, N.R., Siegmund, D.O. (2007), A Modified Bayes Information Criterion With Applications to the Analysis of Comparative Genomic Hybridization Data, Biometrics, 63, 22–32.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.