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Estimating a discrete distribution via histogram selection

Published online by Cambridge University Press:  22 February 2011

Nathalie Akakpo
Nathalie Akakpo*
Affiliation:
Laboratoire de Probabilités et Statistiques, Université Paris Sud XI, Bâtiment 425, 91405 Orsay Cedex, France; nathalie.akakpo@math.u-psud.fr

Abstract

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Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties in the minimax sense. Moreover, its computational complexity is only linear in the length of the sequence. We also use that estimator during the preliminary stage of a hybrid procedure for detecting multiple change-points in the joint distribution of the sequence. That second procedure still satisfies adaptivity properties and can be implemented efficiently. We provide a simulation study and apply the hybrid procedure to the segmentation of a DNA sequence.

Keywords

Adaptive estimatorapproximation resultcategorical variablechange-point detectionminimax estimationmodel selectionnonparametric estimationpenalized least-squares estimation

Information

Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 15: Supplement: In honor of Marc Yor , 2011 , pp. 1 - 29
Copyright
© EDP Sciences, SMAI, 2011

References

Aerts, M. and Veraverbeke, N., Bootstrapping a nonparametric polytomous regression model. Math. Meth. Statist. 4 (1995) 189–200.
Baraud, Y. and Birgé, L., Estimating the intensity of a random measure by histogram type estimators. Prob. Theory Relat. Fields 143 (2009) 239–284. CrossRef
Barron, A., Birgé, L. and Massart, P., Risk bounds for model selection via penalization. Prob. Theory Relat. Fields 113 (1999) 301–413. CrossRef
C. Bennett and R. Sharpley, Interpolation of operators, volume 129 of Pure and Applied Mathematics. Academic Press Inc., Boston, M.A. (1988).
Birgé, L., Model selection via testing: an alternative to (penalized) maximum likelihood estimators. Ann. Inst. H. Poincaré Probab. Statist. 42 (2006) 273–325. CrossRef
L. Birgé, Model selection for Poisson processes, in Asymptotics: Particles, Processes and Inverse Problems, Festschrift for Piet Groeneboom. IMS Lect. Notes Monograph Ser. 55. IMS, Beachwood, USA (2007) 32–64.
Birgé, L. and Massart, P., Minimal penalties for Gaussian model selection. Prob. Theory Relat. Fields 138 (2007) 33–73. CrossRef
Braun, J.V. and Müller, H.-G., Statistical methods for DNA sequence segmentation. Stat. Sci. 13 (1998) 142–162.
Braun, J.V., Braun, R.K. and Müller, H.-G., Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation. Biometrika 87 (2000) 301–314. CrossRef
T.H. Cormen, C.E. Leiserson, R.L. Rivest and C. Stein, Introduction to algorithms. Second edition. MIT Press, Cambridge, MA (2001).
M. Csűrös, Algorithms for finding maximum-scoring segment sets, in Proc. of the 4th international workshop on algorithms in bioinformatics 2004. Lect. Notes Comput. Sci. 3240. Springer, Berlin, Heidelberg (2004) 62–73.
R.A. DeVore and G.G. Lorentz, Constructive approximation. Springer-Verlag, Berlin, Heidelberg (1993).
DeVore, R.A. and Sharpley, R.C., Maximal functions measuring smoothness. Mem. Amer. Math. Soc. 47 (1984) 293.
DeVore, R.A. and Degree, X.M. Yu of adaptive approximation. Math. Comp. 55 (1990) 625–635. CrossRef
Durot, C., Lebarbier, E. and Tocquet, A.-S., Estimating the joint distribution of independent categorical variables via model selection. Bernoulli 15 (2009) 475–507. CrossRef
Fu, Y.-X. and Curnow, R.N., Maximum likelihood estimation of multiple change points. Biometrika 77 (1990) 562–565.
S. Gey S. and E. Lebarbier, Using CART to detect multiple change-points in the mean for large samples. SSB preprint, Research report No. 12 (2008).
Hoebeke, M., Nicolas, P. and Bessières, P., MuGeN: simultaneous exploration of multiple genomes and computer analysis results. Bioinformatics 19 (2003) 859–864. CrossRef
E. Lebarbier, Quelques approches pour la détection de ruptures à horizon fini. Ph.D. thesis, Université Paris Sud, Orsay, 2002.
E. Lebarbier and E. Nédélec, Change-points detection for discrete sequences via model selection. SSB preprint, Research Report No. 9 (2007).
P. Massart, Concentration inequalities and model selection. Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour, July 6–23, 2003. Lect. Notes Math. 1896. Springer, Berlin, Heidelberg (2007).
Nicolas, P. et al., Mining Bacillus subtilis chromosome heterogeneities using hidden Markov models. Nucleic Acids Res. 30 (2002) 1418–1426. CrossRef
Szpankowski, W., Szpankowski, L. and Ren, W., An optimal DNA segmentation based on the MDL principle. Int. J. Bioinformatics Res. Appl. 1 (2005) 3–17. CrossRef