Browse > Article
http://dx.doi.org/10.5351/KJAS.2019.32.4.561

Comparison of multiscale multiple change-points estimators  

Kim, Jaehee (Department of Statistics, Duksung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.4, 2019 , pp. 561-572 More about this Journal
Abstract
We study false discovery rate segmentation (FDRSeg) and simultaneous multiscale change-point estimator (SMUCE) methods for multiscale multiple change-point estimation, and compare empirical behavior via simulation. FSRSeg is based on the control of a false discovery rate while SMUCE used for the multiscale local likelihood ratio tests. FDRSeg seems to work best if the number of change-points is large; however, FDRSeg and SMUCE methods can both provide similar estimation results when there are only a small number of change-points. As a real data application, multiple change-points estimation is done with the well-log data.
Keywords
false discovery rate (FDR); FDRSeg; local likelihood ratio test; multiscale; multiple change-points; SMUCE;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bellman, R. (1957). Dynamic Programming, Princeton University Press, Princeton, NJ.
2 Bellman, R. E. and Dreyfus, S. E. (1962). Applied Dynamic Programming, Princeton University Press, Princeton, NJ.
3 Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of Royal Statistical Society. Series B (Methodological), 57, 289-300.   DOI
4 Birg'e, L. and Massart, P. (2006). Minimal penalties for Gaussian model selection, Probability Theory and Related Fields, 138, 33-73.   DOI
5 Boysen, L., Kempe, A., Liebscher, V., Munk, A., and Wittich, O. (2009). Consistencies and rates of convergence of jump-penalized least squares estimators, The Annals of Statistics, 37, 157-183.   DOI
6 Braun, J. V., Braun, R. K., and Muller, H. G. (2000). Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation, Biometrika, 87, 301-314.   DOI
7 Candes, E. and Tao, T. (2007) The Dantzig selector: statistical estimation when p is much larger than n, The Annals of Statistics, 35, 2313-2351.   DOI
8 Chan, H. P. and Walther, G. (2013). Detection with the scan and the average likelihood ratio, Statistica Sinica, 23, 409-428.
9 Cheon, S. and Kim, J. (2010). Multiple change-point detection of multivariate mean vectors with Bayesian approach, Computational Statistics & Data Analysis, 54, 406-425.   DOI
10 Chernoff, H. and Zacks, S. (1964). Estimating the current mean of a normal distribution which is subjected to change in time, The Annals of Mathematical Statistics, 35, 999-1018.   DOI
11 Davies, P. L., Kovac, A., and Meise, M. (2009). Nonparametric regression, confidence regions and regularization, The Annals of Statistics, 37, 2597-2625.   DOI
12 Dumbgen, L. and Walther, G. (2008). Multiscale inference about a density, The Annals of Statistics, 36, 1758-1785.   DOI
13 Fryzlewicz, P. (2014). Wild binary segmentation for multiple change-point detection, The Annals of Statistics, 42, 2243-2281.   DOI
14 Fearnhead, P. (2006). Exact and efficient Bayesian inference for multiple changepoint problems, Statistics and Computing, 16, 203-213.   DOI
15 Frick, K., Munk, A., and Sieling, H. (2014). Multiscale change-point inference, Journal of the Royal Sta-tistical Society. Series B (Statistical Methodology), with discussion and rejoinder by the authors, 76, 495-580.   DOI
16 Friedman, J., Hastie, T., Hofling, H., and Tibshirani, R. (2007). Pathwise coordinate optimization, Annals of Applied Statistics, 1, 302-332.   DOI
17 Harchaoui, Z. and Levy-Leduc, C. (2010). Multiple change-point estimation with a total variation penalty, Journal of the American Statistical Association, 105, 1480-1493.   DOI
18 Davies, P. L. and Kovac, A. (2001). Local extremes, runs, strings and multiresolution, The Annals of Statistics, 29, 1-65.   DOI
19 Hinkley, D. V. (1970). Inference about the change-point in a sequence of random variables, Biometrika, 57, 1-17.   DOI
20 Huskova, M. and Antoch, J. (2003). Detection of structural changes in regression, Tatra Mountains Mathematical Publications, 26, 201-215.
21 Kander, Z. and Zacks, S. (1966). Test procedures for possible changes in parameters of statistical distributions occurring at unknown time points, The Annals of Mathematical Statistics, 37, 1196-1210.   DOI
22 Kim, J. and Cheon, S. (2010). A Bayesian regime-switching time-series model, Journal of Time Series Analysis, 31, 365-378.   DOI
23 Killick, R., Fearnhead, P., and Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost, Journal of the American Statistical Association, 107, 1590-1598.   DOI
24 Kim, J. and Cheon, S. (2011). Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo, Computational Statistics, 25, 215-239.   DOI
25 Kim, J. and Hart, J. D. (2011). A change-point estimator using local Fourier series, Journal of Nonpara-metric Statistics, 23, 83-98.   DOI
26 Kim, J. H. and Cheon, S. Y. (2013). Bayesian multiple change-point estimation and segmentation, Communications for Statistical Applications and Methods, 20, 439-454.   DOI
27 Kolaczyk, E. D. and Nowark, R. D. (2004). Multiscale likelihood analysis and complexity penalized estimation, Annals of Statistics, 32, 500-527.   DOI
28 Lavielle, M. (2005). Using penalized contrasts for the change-point problem, Signal Processing, 85, 1501-1510.   DOI
29 Lavielle, M. and Moulines, E. (2000). Least-squares estimation of an unknown number of shifts in a time series, Journal of Time Series Analysis, 21, 33-59.   DOI
30 Levy-Leduc, C. and Roueff, F. (2009). Detection and localization of change-points in high-dimensional network traffic data, Annals of Applied Statistics, 3, 637-662.   DOI
31 Li, H., Munk, A., and Sieling, H. (2016). FDR-control in multiscale change-point segmentation, Electronic Journal of Statistics, 10, 918-959.   DOI
32 Olshen, A. B., Venkatraman, E. S., Lucito, R., and Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data, Biostatistics, 5, 557-572.   DOI
33 Yao, Y. C. (1988). Estimating the number of change-points via Schwarz criterion, Statistics & Probability Letters, 6, 181-189.   DOI
34 Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005). Sparsity and smoothness via the fused LASSO, Journal of the Royal Statistical Society Series B (Statistical Methodology), 67, 91-108.   DOI
35 Winkler, G. and Liebscher, V. (2002). Smoothers for discontinuous signals, Journal of Nonparametric Statistics, 14, 203-222.   DOI
36 Worsley, K. J. (1983). The power of likelihood ratio and cumulative sum tests for a change in a binomial probability, Biometrika, 70, 455-464.   DOI
37 Yao, Y. C. and Au, S. T. (1989). Least-squares estimation of a step function, Sankhya: The Indian Journal of Statistics, Series A, 51, 370-381.
38 Zhang, N. R. and Siegmund, D. O. (2007). A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data, Biometrics, 63, 22-32.   DOI
39 Zhang, N. R. and Siegmund, D. O. (2012). Model selection for high-dimensional, multi-sequence changepoint problems, Statistica Sinica, 22, 1507-1538.
40 Siegmund, D. (1988). Confidence sets in change-point problems, International Statistical Review / Revue Internationale de Statistique, 56, 31-48.   DOI