Quelques approches pour la détection de ruptures à horizon fini
Thèses d'Orsay, no. 624 (2002) , 202 p.
@phdthesis{BJHTUP11_2002__0624__P0_0,
     author = {Lebarbier, Emilie},
     title = {Quelques approches pour la d\'etection de ruptures \`a horizon fini},
     series = {Th\`eses d'Orsay},
     publisher = {Universit\'e de Paris-Sud U.F.R. Scientifique d'Orsay},
     number = {624},
     year = {2002},
     language = {fr},
     url = {http://archive.numdam.org/item/BJHTUP11_2002__0624__P0_0/}
}
TY  - BOOK
AU  - Lebarbier, Emilie
TI  - Quelques approches pour la détection de ruptures à horizon fini
T3  - Thèses d'Orsay
PY  - 2002
IS  - 624
PB  - Université de Paris-Sud U.F.R. Scientifique d'Orsay
UR  - http://archive.numdam.org/item/BJHTUP11_2002__0624__P0_0/
LA  - fr
ID  - BJHTUP11_2002__0624__P0_0
ER  - 
%0 Book
%A Lebarbier, Emilie
%T Quelques approches pour la détection de ruptures à horizon fini
%S Thèses d'Orsay
%D 2002
%N 624
%I Université de Paris-Sud U.F.R. Scientifique d'Orsay
%U http://archive.numdam.org/item/BJHTUP11_2002__0624__P0_0/
%G fr
%F BJHTUP11_2002__0624__P0_0
Lebarbier, Emilie. Quelques approches pour la détection de ruptures à horizon fini. Thèses d'Orsay, no. 624 (2002), 202 p. http://numdam.org/item/BJHTUP11_2002__0624__P0_0/

[1] Akaike, H. Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory (Tsahkadsor, 1971). Akadémiai Kiadó, Budapest, 1973, pp. 267-281. | MR | Zbl

[2] Akaike, H. A new look at the statistical model identification. IEEE Trans. Automatic Control AC-19 (1974), 716-723. System identification and time-series analysis. | MR | Zbl | DOI

[3] Avery, P., and Henderson9, D. Detecting a changed segment in DNA sequences. J. Roy. Statist. Soc. Ser. C 48, 4 (1999), 489-503. | MR | Zbl

[4] Basseville, M., and Nikiforov, N. The Detection of abrupt changes - Theory and applications. Prentice-Hall : Information and System sciences series, 1993. | MR | Zbl

[5] Bellalah, M., and Lavielle, M. A simple decomposition of empirical distributions and its applications in asset pricing, (submitted) (1997).

[6] Besag, J., Green, P., Hidgon, D., and Mengersen, K. Baesyian computation and stochastic systems. Statistical Science 10 (1995), 3-66. | MR | Zbl

[7] Birgé, L., and Massart, P. Gaussian model selection. J. Eur. Math. Soc. 3 (2001), 203-268. | MR | Zbl | DOI

[8] Birgé, L., and Massart, P. A generalized Cp criterion for Gaussian model selection. Tech. rep., Publication Université Paris-VI, 2001.

[9] Birgé, L., and Rozenholc, Y. How many bins should be put in a regular histogram. Tech. rep., Publication Université Paris-VI, 1999.

[10] Biscay, R., Lavielle, M., González A., Clark, I., and Valdés, P. Maximum a posteriori estimation of change points in the EEG. Int. J. of Bio-Medical Computing 38 (1995), 189-196. | DOI

[11] Braun, J. V., Braun, R. K., and Müller, H.-G. Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation. Statistical Science 87, 2 (2000), 142-162.

[12] Braun, J. V., and Müller, H.-G. Statistical methods for DNA sequence segmentation. Biometrika 13, 2 (1998), 301-314.

[13] Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. Classification And Regression Trees. Chapman & Hall, 1984.

[14] Brodsky, B., and Darkhovsky, B. Nonparametric methods in change-point problems. Kluwer Academic Publishers, the Netherlands, 1993. | MR | Zbl | DOI

[15] Cappé, O., Doucet, A., Lavielle, M., and Moulines, E. Methods for blind maximum-likelihood linear system identification. Signal Processing 73 (1999), 3-25.

[16] Carter, R. L., and Blight, B. J. N. A Bayesian change-point problem with an application to the prediction and detection of ovulation in women. Biometrics 37, 4 (1981), 743-751. | MR | Zbl | DOI

[17] Casella, G., and Robert, C. Rao-blackwellisation of sampling schemes. Biometrika 83 (1996), 81-94. | MR | Zbl | DOI

[18] Castellan, G. Modified akaike's criterion for histogram density estimation. Tech. Rep. 61, Université Paris XI, 1999.

[19] Chong, T. T.-L. Estimating the locations and number of change points by the sample-splitting method. Statist. Papers 42, 1 (2001), 53-79. | MR | Zbl

[20] Churchill, G. Stochastic Models for Heterogeneous DNA Sequences. Bulletin of Mathematical Biology 51, 1 (1989), 79-94. | MR | Zbl | DOI

[21] Csorgo, M., and Horvàth, L. Limit theorems in change-point analysis. Tech. rep., U.K. : Wiley, 1997.

[22] Delyon, B., Lavielle, M., and Moulines, E. Convergence of a stochastic approximation version of the EM algorithm. The Annals of Stat. 27, 1 (1999), 94-128. | MR | Zbl | DOI

[23] Dempster, A., Laird, N., and Rubin, D. Maximum-likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc. B 39 (1977), 1-38. | MR | Zbl

[24] Duflo, M. Algorithmes Stochastiques. SMAI, Springer, 1996. | MR | Zbl

[25] Engl, H., and Grever, W. Using the L-curve for determining optimal regularization parameters. Numer. Math. 69, 1 (1994), 25-31. | MR | Zbl | DOI

[26] Fu, Y.-X., and Curnow, R. N. Maximum likelihood estimation of multiple change points. Biometrika 77, 3 (1990), 563-573. | MR | Zbl | DOI

[27] Geman, S., and Geman, D. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. on Pattern Anal. Machine Intell. 6 (1984), 721-741. | Zbl | DOI

[28] Gey, S., and Nedelec, E. Model selection for CART regression trees. Tech. Rep. 56, Université Paris XI, 2001.

[29] Ghorbanzadeh, D. Un test de détection de rupture de la moyenne dans un modèle gaussien. Rev. Statist. Appl. 43, 2 (1995), 67-76. | MR | Zbl | Numdam

[30] Ghorbanzadeh, D. Un test de détection de rupture de la moyenne dans un modèle gaussien. Rev. Statist. Appl. 43, 2 (1995), 67-76. | MR | Zbl | Numdam

[31] Green, P. Reversible jump mcmc computation and bayesian model determination. Biometrika 82 (1995), 711-732. | MR | Zbl | DOI

[32] Hall, P., Kay, J., and Titterington, D. Asymptotically optimal difference-based estimation of variance in nonparametric regression. Biometrika 77 (1990), 521-8. | MR | Zbl | DOI

[33] Hanke, M. Limitations of the L-curve method in ill-posed problems. BIT 36, 2 (1996), 287-301. | MR | Zbl

[34] Hansen, P. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Rev. 34, 4 (1992), 561-580. | MR | Zbl | DOI

[35] Hansen, P., and O'Leary, D. The use of the L-curve in the regularization of discrete ill-posed problems. SIAM J. Sci. Comput. 14, 6 (1993), 1487-1503. | MR | Zbl | DOI

[36] J.L. Olivier, R. Román-Roldán, J. P., and Bernaola-Galván, P. Segment : identifying compositional domains in DNA sequences. Bioinformatics 15, 12 (1999), 974-979. | DOI

[37] Kay, S. M. Fundamentals of statistical signal processing - Detection theory, vol. II. Prentice Hall signal processing series, 1998.

[38] Lavielle, M. A stochastic procedure for parametric and non-parametric estimation in the case of incomplete data. Signal Processing 42 (1995), 3-17. | Zbl | DOI

[39] Lavielle, M. Optimal segmentation of random processes. IEEE Trans. on Signal Processing 46, 5 (1998), 1365-1373. | DOI

[40] Lavielle, M. Detection of multiple changes in a sequence of dependent variables. Stoch. Proc. and Appl. 83 (1999), 79-102. | MR | Zbl | DOI

[41] Lavielle, M., and Lebarbier, E. An application of MCMC methods for the multiple change-points problem. Signal processing 81 (2001), 39-53. | Zbl | DOI

[42] Lavielle, M., and Moulines, E. Least Squares estimation of an unknown number of shifts in a time series. Jour. of Time Series Anal. 21 (2000), 33-59. | MR | Zbl | DOI

[43] Lebarbier, E. Quelques approches pour la détection de ruptures à horizon fini. PhD thesis, Université Paris XI, 2002.

[44] Lee, C.-B. Estimating the number of change points in exponential families distributions. Scand. J. Statist. 24, 2 (1997), 201-210. | MR | Zbl | DOI

[45] Letué, F. Modèle de Cox : estimation par sélection de modèle et modèle de chocs bivarié. PhD thesis, Université de Paris-Sud, 2000.

[46] Lezaud, P. Chernoff-type bound for finite Markov chains. Ann. Appl. Probab. 8, 3 (1998), 849-867. | MR | Zbl

[47] Mallows, C. Some comments on Cp. Technometrics 15 (1974), 661-675. | Zbl

[48] Massart, P. Some Applications of Concentration Inequalities to Statistics. Annales de la Faculté des Sciences de Toulouse IX, 2 (2000), 245-303. | MR | Zbl | Numdam

[49] Métivier, M., and Priouret, P. Théorèmes de convergence presque sure pour une classe d'algorithmes stochastiques à pas décroissant. Probab. Theory Related Fields 74, 3 (1987), 403-428. | MR | Zbl

[50] Meyn, S. P., and Tweedie, R. L. Markov chains and stochastic stability. Springer-Verlag London Ltd., London, 1993. | MR

[51] Miao, B. Q., and Zhao, L. C. On detection of change points when the number is unknown. Chinese J. Appl. Probab. Statist. 9, 2 (1993), 138-145. | MR | Zbl

[52] Muri, F. Comparaison d'algorithmes d'identification de chaînes de Markov cachées et application à la détection de régions homogènes dans les séquences d'ADN. PhD thesis, Université René Descartes, Paris V, 1997.

[53] Normand, S.-L. T., and Doksum, K. Empirical Bayes procedures for a change point problem with application to HIV/AIDS data. In Empirical Bayes and likelihood inference (Montreal, QC, 1997). Springer, New York, 2001, pp. 67-79. | MR | DOI

[54] Picard, D. Testing and estimating change points in time series. J. Applied Prob. 17 (1985), 841-867. | MR | Zbl | DOI

[55] Rao, C. R. Linear statistical inference and its applications, second ed. John Wiley & Sons, New York-London-Sydney, 1973. Wiley Series in Probability and Mathematical Statistics. | MR

[56] R.J. Boys, D. H., and Wilkinson, D. Detecting homogeneous segments in DNA sequences by using hidden Markov models. J. Roy. Statist. Soc. Ser. C 48 4 (1999), 489-503.

[57] Robert, C. Méthodes de Monte Carlo par Chaînes de Markov. Statistique mathématique et Probabilité. Economica, 1996. | MR | Zbl

[58] Robert, C. P., Ed. Discretization and MCMC convergence assessment Springer-Verlag, New York, 1998. | MR | Zbl

[59] Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6 (1978), 461-464. | MR | Zbl | DOI

[60] Smith, A. F. M. Change-point problems : approaches and applications. In Bayesian statistics (Valencia, 1979). Univ. Press, Valencia, 1980, pp. 83-98. | MR | Zbl

[61] Tibshirani, R., Walther, G., and Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B Stat. Methodol. 63, 2 (2001), 411-423. | MR | Zbl | DOI

[62] Tourneret, J., and Chabert, M. Off-line detection and estimation of abrupt changes corrupted by multiplicative colored gaussian noise. Tech. rep., Proc. of ICASSP'97, Munich, April, 1997.

[63] Tourneret, J., Coulon, M., and Doisy, M. Least Squares estimation of multiple abrupt changes contamined by multiplicative noise using mcmc. Tech. rep., Proc. of HOS'99, Caesarea, June, 1999.

[64] Vogel, C. R. Non-convergence of the L-curve regularization parameter selection method. Inverse Problems 12, 4 (1996), 535-547. | MR | Zbl | DOI

[65] Vostrikova, L. Detecting "disorder" in multidimensional random processes. Soviet. Math. Dokl. 24 (1981), 55-59. | Zbl

[66] Yao, Y. Estimating the number of change-points via Schwarz criterion. Stat. & Probab. Lett. 6 (1988), 181-189. | MR | Zbl | DOI

[67] Yashchin, E. Change-point models in industrial applications. In Proceedings of the Second World Congress of Nonlinear Analysts, Part 7 (Athens, 1996) (1997), vol. 30, pp. 3997-4006. | MR | Zbl