A change-point detection and clustering method in the recurrent-event context

References

• Li Q, Guo F, Klauer S, et al. Evaluation of risk change-point for novice teenage drivers. Accid Anal Prev. 2017;108:139–146. doi: 10.1016/j.aap.2017.08.007
   PubMed Web of Science ®Google Scholar
• Frobish D, Ebrahimi N. Parametric estimation of change-points for actual event data in recurrent events models. Comput Stat Data Anal. 2009;53:671–682. doi: 10.1016/j.csda.2008.08.022
   Web of Science ®Google Scholar
• Raftery AE, Akman VE. Bayesian analysis of a poisson process with a change-point. Biometrika. 198673:85–89. doi: 10.1093/biomet/73.1.85
   Web of Science ®Google Scholar
• Ruggeri F, Sivaganesan S. On modeling change points in non-homogeneous Poisson processes. Stat Inference Stoch Process. 2005;8:311–329. doi: 10.1007/s11203-005-6100-y
   Google Scholar
• Ross SM. Simulation. 4th ed. Cambridge (MA): Academic Press; 2006.
   Google Scholar
• Cruz-Juárez JA, Reyes-Cervantes H, Rodrigues ER. Analysis of ozone behaviour in the city of puebla-mexico using non-homogeneous Poisson models with multiple change-points. J Environ Prot. 2016;7:1886. doi: 10.4236/jep.2016.712149
   Google Scholar
• Frobish D, Ebrahimi N, Pham D. Semiparametric estimation of a change-point for recurrent events data. Commun Stat Simul Comput. 2016;45:3339–3349. doi: 10.1080/03610918.2014.944654
   Web of Science ®Google Scholar
• Lawless J, Zhan M. Analysis of interval-grouped recurrent-event data using piecewise constant rate functions. Can J Stat. 1998;26:549–565. doi: 10.2307/3315717
   Web of Science ®Google Scholar
• Achcar JA, Loibel S, Andrade M. Interfailure data with constant hazard function in the presence of change-points. REVSTAT. 2007;5:209–226.
   Web of Science ®Google Scholar
• Gupta A, Baker JW. A Bayesian change point model to detect changes in event occurrence rates, with application to induced seismicity. 12th International Conference on Applications of Statistics and Probability in Civil Engineering; Vancouver, Canada: ICASP12; July 2015.
   Google Scholar
• Montoya-Noguera S, Wang Yu. Bayesian identification of multiple seismic change points and varying seismic rates caused by induced seismicity. Geophys Res Lett. 2017;44:3509–3516. doi: 10.1002/2016GL072266
   Web of Science ®Google Scholar
• West R, Odgen R. Continuous-time estimation of a change-point in a Poisson process. J Stat Comput Simul. 1997;56:293–302. doi: 10.1080/00949659708811795
   Web of Science ®Google Scholar
• Li Q, Guo F, Kim I, et al. A Bayesian finite mixture change-point model for assessing the risk of novice teenage drivers. J Appl Stat. 2018;45:604–625. doi: 10.1080/02664763.2017.1288202
   PubMed Web of Science ®Google Scholar
• Perets T. Clustering of lines. Ra'anana, Israel: Open University of Israel; 2011.
   Google Scholar
• Dass SC, Lim CY, Maiti T. Clustering curves based on change point analysis: a nonparametric Bayesian approach. Stat Sin. 2015;25:677–708.
   Web of Science ®Google Scholar
• Lloyd S. Least squares quantization in PCM. IEEE Trans Inf Theory. 1982;28(2):129–137. doi: 10.1109/TIT.1982.1056489
   Web of Science ®Google Scholar
• Fraley C, Raftery AE. How many clusters? which clustering method? answers via model-based cluster analysis. Comput J. 1998;41:578–588. doi: 10.1093/comjnl/41.8.578
   Web of Science ®Google Scholar
• Akaike H. Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike; Springer, 1998. New York, NY p. 199–213.
   Google Scholar
• Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464. doi: 10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Kassambara A. Practical guide to cluster analysis in R: unsupervised machine learning. Vol. 1, STHDA; 2017.
   Google Scholar
• Sugar CA, James GM. Finding the number of clusters in a dataset: an information-theoretic approach. J Am Stat Assoc. 2003;98:750–763. doi: 10.1198/016214503000000666
   Web of Science ®Google Scholar
• Thompson W. Point process models with applications to safety and reliability. New York (NY): Springer Science & Business Media; 2012.
   Google Scholar
• Hartigan JA, Wong MA. Algorithm as 136: a k-means clustering algorithm. J Royal Stat Soc Ser C (Appl Stat). 1979;28:100–108.
   Google Scholar
• Jain AK, Dubes RC. Algorithms for clustering data. Englewook Cliff (NJ): Prentice-Hall, Inc.; 1988.
   Google Scholar
• Davison AC, Hinkley DV. Bootstrap methods and their application. Vol. 1. Cambridge, United Kingdom: Cambridge University Press; 1997.
   Google Scholar
• Breiman L. Classification and regression trees. Boca Raton (FL): Chapman & Hall/CRC; 2017.
   Google Scholar
• Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65. doi: 10.1016/0377-0427(87)90125-7
   Web of Science ®Google Scholar
• Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B (Stat Methodol). 2001;63:411–423. doi: 10.1111/1467-9868.00293
   Google Scholar
• Klein RW, Roberts SD. A time-varying Poisson arrival process generator. Simulation. 1984;43:193–195. doi: 10.1177/003754978404300406
   Google Scholar
• Carlin BP, Gelfand AE, FM. Smith A. Hierarchical Bayesian analysis of changepoint problems. Appl Stat. 1992;41:389–405. doi: 10.2307/2347570
   Web of Science ®Google Scholar
• Green PJ. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika. 1995;82:711–732. doi: 10.1093/biomet/82.4.711
   Web of Science ®Google Scholar
• Seddon M. The long, slow death of the UK coal industry. Guardian 2013 Apr 10;
   Google Scholar