Issue
ESAIM: PS
Volume 6, 2002
New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
Page(s) 189 - 209
Section New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
DOI https://doi.org/10.1051/ps:2002011
Published online 15 November 2002
  1. H. Akaike, A new look at the statistical model identification. IEEE Trans. Automat. Control AC-19 (1974) 716-723. System identification and time-series analysis. [Google Scholar]
  2. A. Antoniadis, I. Gijbels and B. MacGibbon, Non-parametric estimation for the location of a change-point in an otherwise smooth hazard function under random censoring. Scand. J. Statist. 27 (2000) 501-519. [CrossRef] [MathSciNet] [Google Scholar]
  3. Z.D. Bai, C.R. Rao and Y. Wu, Model selection with data-oriented penalty. J. Statist. Plann. Inference 77 (1999) 103-117. [CrossRef] [MathSciNet] [Google Scholar]
  4. A. Barron, L. Birgé and P Massart, Risk bounds for model selection via penalization. Probab. Theory Related Fields 113 (1999) 301-413. [Google Scholar]
  5. M. Basseville and I.V. Nikiforov, Detection of abrupt changes: Theory and application. Prentice Hall Inc. (1993). [Google Scholar]
  6. B.E. Brodsky and B.S. Darkhovsky, Nonparametric methods in change-point problems. Kluwer Academic Publishers Group (1993). [Google Scholar]
  7. E. Carlstein, H.-G. Müller and D. Siegmund, Change-point problems. Institute of Mathematical Statistics, Hayward, CA (1994). Papers from the AMS-IMS-SIAM Summer Research Conference held at Mt. Holyoke College, South Hadley, MA July 11-16, 1992. [Google Scholar]
  8. D. Dacunha-Castelle and E. Gassiat, The estimation of the order of a mixture model. Bernoulli 3 (1997) 279-299. [CrossRef] [MathSciNet] [Google Scholar]
  9. J. Dedecker, Exponential inequalities and functional central limit theorems for random fields. ESAIM P&S 5 (2001) 77. [CrossRef] [EDP Sciences] [Google Scholar]
  10. P. Doukhan, Mixing. Springer-Verlag, New York (1994). Properties and examples. [Google Scholar]
  11. M. Lavielle, On the use of penalized contrasts for solving inverse problems. Application to the DDC (Detection of Divers Changes) problem (submitted). [Google Scholar]
  12. M. Lavielle, Detection of multiple changes in a sequence of dependent variables. Stochastic Process. Appl. 83 (1999) 79-102. [CrossRef] [MathSciNet] [Google Scholar]
  13. M. Lavielle and E. Lebarbier, An application of MCMC methods for the multiple change-points problem. Signal Process. 81 (2001) 39-53. [CrossRef] [Google Scholar]
  14. M. Lavielle and C. Lude na, The multiple change-points problem for the spectral distribution. Bernoulli 6 (2000) 845-869. [CrossRef] [MathSciNet] [Google Scholar]
  15. M. Lavielle and E. Moulines, Least-squares estimation of an unknown number of shifts in a time series. J. Time Ser. Anal. 21 (2000) 33-59. [CrossRef] [MathSciNet] [Google Scholar]
  16. G. Lugosi, Lectures on statistical learning theory. Presented at the Garchy Seminar on Mathematical Statistics and Applications, available at http://www.econ.upf.es/~lugosi (2000). [Google Scholar]
  17. E. Mammen and A.B. Tsybakov, Asymptotical minimax recovery of sets with smooth boundaries. Ann. Statist. 23 (1995) 502-524. [Google Scholar]
  18. P. Massart, Some applications of concentration inequalities to statistics. Ann. Fac. Sci. Toulouse Math. (6) 9 (2000) 245-303. [MathSciNet] [Google Scholar]
  19. F. Móricz, A general moment inequality for the maximum of the rectangular partial sums of multiple series. Acta Math. Hungar. 41 (1983) 337-346. [CrossRef] [MathSciNet] [Google Scholar]
  20. F.A. Móricz, R.J. Serfling and W.F. Stout, Moment and probability bounds with quasisuperadditive structure for the maximum partial sum. Ann. Probab. 10 (1982) 1032-1040. [CrossRef] [MathSciNet] [Google Scholar]
  21. V.V. Petrov, Limit theorems of probability theory. The Clarendon Press Oxford University Press, New York (1995). Sequences of independent random variables, Oxford Science Publications. [Google Scholar]
  22. E. Rio, Théorie asymptotique des processus aléatoires faiblement dépendants. Springer (2000). [Google Scholar]
  23. G. Schwarz, Estimating the dimension of a model. Ann. Statist. 6 (1978) 461-464. [Google Scholar]
  24. R.J. Serfling, Contributions to central limit theory for dependent variables. Ann. Math. Statist. 39 (1968) 1158-1175. [CrossRef] [MathSciNet] [Google Scholar]
  25. M. Talagrand, New concentration inequalities in product spaces. Invent. Math. 126 (1996) 505-563. [CrossRef] [MathSciNet] [Google Scholar]
  26. A.W. van der Vaart, Asymptotic statistics. Cambridge University Press (1998). [Google Scholar]
  27. A.W. van der Vaart and J.A. Wellner, Weak convergence and empirical processes. Springer-Verlag, New York (1996). With applications to statistics. [Google Scholar]
  28. V.N. Vapnik, Statistical learning theory. John Wiley & Sons Inc., New York (1998). [Google Scholar]
  29. Y.-C. Yao, Estimating the number of change-points via Schwarz's criterion. Statist. Probab. Lett. 6 (1988) 181-189. [Google Scholar]

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