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Bibliography

Major publications by the team in recent years
  • 1G. Biau, F. Cérou, A. Guyader.

    On the rate of convergence of the bagged nearest neighbor estimate, in: Journal of Machine Learning Research, February 2010, vol. 11, p. 687–712.

    http://jmlr.csail.mit.edu/papers/v11/biau10a.html
  • 2G. Biau, F. Cérou, A. Guyader.

    On the rate of convergence of the functional k–nearest neighbor estimates, in: IEEE Transactions on Information Theory, April 2010, vol. IT–56, no 4, p. 2034–2040.

    http://dx.doi.org/10.1109/TIT.2010.2040857
  • 3F. Cérou, A. Guyader.

    Nearest neighbor classification in infinite dimension, in: ESAIM : Probability and Statistics, 2006, vol. 10, p. 340–355.

    http://dx.doi.org/10.1051/ps:2006014
  • 4F. Cérou, A. Guyader.

    Adaptive multilevel splitting for rare event analysis, in: Stochastic Analysis and Applications, March 2007, vol. 25, no 2, p. 417–443.

    http://dx.doi.org/10.1080/07362990601139628
  • 5F. Cérou, P. Del Moral, F. Le Gland, P. Lezaud.

    Genetic genealogical models in rare event analysis, in: ALEA, Latin American Journal of Probability and Mathematical Statistics, 2006, vol. 1, p. 181–203 (electronic), Paper 01–08.
  • 6T. Furon, A. Guyader, F. Cérou.

    On the design and optimization of Tardos probabilistic fingerprinting codes, in: 10th International Workshop on Information Hiding, Santa Barbara, Berlin, K. Solanki, K. Sullivan, U. Madhow (editors), Lecture Notes in Computer Science, Springer, Berlin, May 2008, vol. 5284, p. 341–356.

    http://dx.doi.org/10.1007/978-3-540-88961-8_24
  • 7F. Le Gland, L. Mevel.

    Exponential forgetting and geometric ergodicity in hidden Markov models, in: Mathematics of Control, Signals, and Systems, 2000, vol. 13, no 1, p. 63–93.

    http://dx.doi.org/10.1007/PL00009861
  • 8F. Le Gland, N. Oudjane.

    A sequential algorithm that keeps the particle system alive, in: Stochastic Hybrid Systems : Theory and Safety Critical Applications, Berlin, H. A. P. Blom, J. Lygeros (editors), Lecture Notes in Control and Information Sciences, Springer–Verlag, Berlin, 2006, no 337, p. 351–389.

    http://dx.doi.org/10.1007/11587392_11
  • 9C. Musso, N. Oudjane, F. Le Gland.

    Improving regularized particle filters, in: Sequential Monte Carlo Methods in Practice, New York, A. Doucet, N. de Freitas, N. J. Gordon (editors), Statistics for Engineering and Information Science, Springer–Verlag, New York, 2001, chap. 12, p. 247–271.
Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 10R. Pastel.

    Estimation de probabilités d'évènements rares et de quantiles extrêmes. Applications dans le domaine aérospatial, Université de Rennes 1, Rennes, February 2012.

    http://tel.archives-ouvertes.fr/tel-00728108

Articles in International Peer-Reviewed Journals

  • 11P. Ailliot, C. Maisondieu, V. Monbet.

    Dynamical partitioning of directional ocean wave spectra, in: Probabilistic Engineering Mechanics, 2013, to appear.
  • 12P. Ailliot, V. Monbet.

    Markov switching autoregressive models for wind time series, in: Environmental Modelling & Software, April 2012, vol. 30, p. 92–101.

    http://dx.doi.org/10.1016/j.envsoft.2011.10.011
  • 13M. Benaïm, S. Le Borgne, F. Malrieu, P.–A. Zitt.

    Quantitative ergodicity for some switched dynamical systems, in: Electronic Communications in Probability, 2012, vol. 17, no 56, p. 1–14.

    http://dx.doi.org/10.1214/ECP.v17-1932
  • 14F. Cérou, P. Del Moral, T. Furon, A. Guyader.

    Sequential Monte Carlo for rare event estimation, in: Statistics and Computing, 2012, vol. 22, no 3, p. 795–908.

    http://dx.doi.org/10.1007/s11222-011-9231-6
  • 15J. Fontbona, H. Guérin, F. Malrieu.

    Quantitative estimates for the long–time behavior of an ergodic variant of the telegraph process, in: Advances in Applied Probability, December 2012, vol. 44, no 4, p. 977–994.

    http://dx.doi.org/10.1239/aap/1354716586
  • 16D. Jacquemart, J. Morio.

    Conflict probability estimation between aircraft with dynamic importance splitting, in: Safety Science, January 2013, vol. 51, no 1, p. 94–100.

    http://dx.doi.org/10.1016/j.ssci.2012.05.010
  • 17J. Krystul, F. Le Gland, P. Lezaud.

    Sampling per mode for rare event simulation in switching diffusions, in: Stochastic Processes and their Applications, July 2012, vol. 122, no 7, p. 2639–2667.

    http://dx.doi.org/10.1016/j.spa.2012.04.011
  • 18J. Morio, R. Pastel, F. Le Gland.

    Missile target accuracy estimation with importance splitting, in: Aerospace Science and Technology, 2013, to appear.

    http://dx.doi.org/10.1016/j.ast.2011.12.006
  • 19J. Morio, R. Pastel.

    Plug-in estimation of d–dimensional density minimum volume set of a rare event in a complex system, in: Proceedings of the Institution of Mechanical Engineers, Part O : Journal of Risk and Reliability, June 2012, vol. 226, no 3, p. 337–345.

    http://dx.doi.org/10.1177/1748006X11426973
  • 20R. Pastel, J. Morio, F. Le Gland.

    Improvement of satellite conflict prediction reliability through use of the adaptive splitting technique, in: Proceedings of the Institution of Mechanical Engineers, Part G : Journal of Aerospace Engineering, 2013, to appear.

    http://dx.doi.org/10.1177/0954410012467725

International Conferences with Proceedings

  • 21P. Bui–Quang, C. Musso, F. Le Gland.

    Multidimensional Laplace formulas for nonlinear Bayesian estimation, in: Proceedings of the 20th European Signal Processing Conference, Bucharest 2012, EURASIP, August 2012.
  • 22T. Furon, A. Guyader, F. Cérou.

    Decoding fingerprinting using the Markov chain Monte Carlo method, in: Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security, Tenerife 2012, IEEE, December 2012.

    http://hal.inria.fr/hal-00757152
  • 23A. Lepoutre, O. Rabaste, F. Le Gland.

    A particle filter for target arrival detection and tracking in track–before–detect, in: Proceedings of the 2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, Bonn 2012, September 2012, p. 13–18.

    http://dx.doi.org/10.1109/SDF.2012.6327901
  • 24A. Lepoutre, O. Rabaste, F. Le Gland.

    Optimized instrumental density for particle filter in track–before–detect, in: Proceedings of the 9th IET Data Fusion Target Tracking Conference, London 2012, IET, May 2012.

    http://dx.doi.org/10.1049/cp.2012.0418
  • 25O. Rabaste, C. Riché, A. Lepoutre.

    Long–time coherent integration for low SNR target via particle filter in track–before–detect, in: Proceedings of the 15th International Conference on Information Fusion, Singapore 2012, ISIF, July 2012, p. 127–134.

Scientific Books (or Scientific Book chapters)

  • 26M. Chouchane, S. Paris, F. Le Gland, M. Ouladsine.

    Splitting method for spatio–temporal sensors deployment in underwater systems, in: Proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimization, Málaga 2012, J.–K. Hao, M. Middendorf (editors), Lecture Notes in Computer Science, Springer, April 2012, vol. 7245, p. 243–254.

    http://dx.doi.org/10.1007/978-3-642-29124-1_21
  • 27P.–A. Cornillon, A. Guyader, F. Husson, N. Jégou, J. Josse, M. Kloareg, É. Matzner–Løber, L. Rouvière.

    R for statistics, Chapman and Hall/CRC Press, Boca Raton, 2012.

    http://hal.inria.fr/hal-00691458

Other Publications

References in notes
  • 34A. Doucet, N. de Freitas, N. J. Gordon (editors)

    Sequential Monte Carlo methods in practice, Statistics for Engineering and Information Science, Springer–Verlag, New York, 2001.
  • 35M. S. Arulampalam, S. Maksell, N. J. Gordon, T. Clapp.

    A tutorial on particle filters for online nonlinear / non–Gaussian Bayesian tracking, in: IEEE Transactions on Signal Processing, February 2002, vol. SP–50, no 2 (Special issue on Monte Carlo Methods for Statistical Signal Processing), p. 174–188.
  • 36M. Baldé, U. Boscain, P. Mason.

    A note on stability conditions for planar switched systems, in: International Journal of Control, October 2009, vol. 82, no 10, p. 1882–1888.

    http://dx.doi.org/10.1080/00207170902802992
  • 37O. Cappé, S. J. Godsill, É. Moulines.

    An overview of existing methods and recent advances in sequential Monte Carlo, in: Proceedings of the IEEE, May 2007, vol. 95, no 5, p. 899–924.
  • 38O. Cappé, É. Moulines, T. Rydén.

    Inference in hidden Markov models, Springer Series in Statistics, Springer–Verlag, New York, 2005.
  • 39P.–T. De Boer, D. P. Kroese, S. Mannor, R. Y. Rubinstein.

    A tutorial on the cross–entropy method, in: Annals of Operations Research, January 2005, vol. 134 (Special issue on the Cross-Entropy Method for Combinatorial Optimization, Rare Event Simulation and Neural Computation), no 1, p. 19–67.
  • 40P. Del Moral.

    Feynman–Kac formulae. Genealogical and interacting particle systems with applications, Probability and its Applications, Springer–Verlag, New York, 2004.
  • 41P. Del Moral, A. Guionnet.

    On the stability of interacting processes with applications to filtering and genetic algorithms, in: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2001, vol. 37, no 2, p. 155–194.
  • 42P. Del Moral, L. Miclo.

    Branching and interacting particle systems approximations of Feynman–Kac formulae with applications to nonlinear filtering, in: Séminaire de Probabilités XXXIV, Berlin, J. Azéma, M. Émery, M. Ledoux, M. Yor (editors), Lecture Notes in Mathematics, Springer–Verlag, Berlin, 2000, vol. 1729, p. 1–145.
  • 43R. Douc, C. Matias.

    Asymptotics of the maximum likelihood estimator for general hidden Markov models, in: Bernoulli, June 2001, vol. 7, no 3, p. 381–420.
  • 44G. Evensen.

    Ensemble Kalman filter : theoretical formulation and practical implementations, in: Ocean Dynamics, 2003, vol. 53, p. 343–367.
  • 45G. Evensen.

    Sampling strategies and square root analysis schemes for the EnKF, in: Ocean Dynamics, 2004, vol. 54, p. 539–560.
  • 46G. Evensen.

    Data assimilation. The ensemble Kalman filter, Springer–Verlag, Berlin, 2006.
  • 47D. Fox, J. Hightower, L. Liao, D. Schulz, G. Borriello.

    Bayesian filtering for location estimation, in: IEEE Pervasive Computing, July/September 2003, vol. 2, no 3, p. 24–33.
  • 48D. Fox, S. Thrun, W. Burgard, F. Dellaert.

    Particle filters for mobile robot localization, in: Sequential Monte Carlo Methods in Practice, New York, A. Doucet, N. de Freitas, N. J. Gordon (editors), Statistics for Engineering and Information Science, Springer–Verlag, New York, 2001, chap. 19, p. 401–428.
  • 49D. Frenkel, B. Smit.

    Understanding molecular simulation. From algorithms to applications, Computational Science Series, 2nd, Academic Press, San Diego, 2002, vol. 1.
  • 50P. Glasserman.

    Monte Carlo methods in financial engineering, Applications of Mathematics, Springer–Verlag, New York, 2004, vol. 53.
  • 51P. Glasserman, P. Heidelberger, P. Shahabuddin, T. Zajic.

    Multilevel splitting for estimating rare event probabilities, in: Operations Research, July–August 1999, vol. 47, no 4, p. 585–600.
  • 52N. J. Gordon, D. J. Salmond, A. F. M. Smith.

    Novel approach to nonlinear / non–Gaussian Bayesian state estimation, in: IEE Proceedings, Part F, April 1993, vol. 140, no 2, p. 107–113.
  • 53F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, P.–J. Nordlund.

    Particle filters for positioning, navigation, and tracking, in: IEEE Transactions on Signal Processing, February 2002, vol. SP–50, no 2 (Special issue on Monte Carlo Methods for Statistical Signal Processing), p. 425–437.
  • 54M. Isard, A. Blake.

    Condensation — Conditional density propagation for visual tracking, in: International Journal of Computer Vision, August 1998, vol. 29, no 1, p. 5–28.
  • 55M. R. James, F. Le Gland.

    Consistent parameter estimation for partially observed diffusions with small noise, in: Applied Mathematics & Optimization, July/August 1995, vol. 32, no 1, p. 47–72.
  • 56M. Joannides, F. Le Gland.

    Small noise asymptotics of the Bayesian estimator in nonidentifiable models, in: Statistical Inference for Stochastic Processes, 2002, vol. 5, no 1, p. 95–130.
  • 57G. Kitagawa.

    Monte Carlo filter and smoother for non–Gaussian nonlinear state space models, in: Journal of Computational and Graphical Statistics, 1996, vol. 5, no 1, p. 1–25.
  • 58Y. A. Kutoyants.

    Identification of dynamical systems with small noise, Mathematics and its Applications, Kluwer Academic Publisher, Dordrecht, 1994, vol. 300.
  • 59H. R. Künsch.

    Recursive Monte Carlo filters : Algorithms and theoretical analysis, in: The Annals of Statistics, October 2005, vol. 33, no 5, p. 1983–2021.
  • 60P. L'Écuyer, V. Demers, B. Tuffin.

    Rare events, splitting, and quasi–Monte Carlo, in: ACM Transactions on Modeling and Computer Simulation, April 2007, vol. 17, no 2 (Special issue honoring Perwez Shahabuddin), Article 9.
  • 61L. Le Cam.

    Asymptotic methods in statistical decision theory, Springer Series in Statistics, Springer–Verlag, New York, 1986.
  • 62F. Le Gland, V. Monbet, V.–D. Tran.

    Large sample asymptotics for the ensemble Kalman filter, in: The Oxford Handbook of Nonlinear Filtering, Oxford, D. O. Crisan, B. L. Rozovskii (editors), Oxford University Press, Oxford, 2011, chap. 22, p. 598–631.
  • 63F. Le Gland, N. Oudjane.

    A robustification approach to stability and to uniform particle approximation of nonlinear filters : the example of pseudo-mixing signals, in: Stochastic Processes and their Applications, August 2003, vol. 106, no 2, p. 279-316.
  • 64F. Le Gland, N. Oudjane.

    Stability and uniform approximation of nonlinear filters using the Hilbert metric, and application to particle filters, in: The Annals of Applied Probability, February 2004, vol. 14, no 1, p. 144–187.
  • 65F. Le Gland, B. Wang.

    Asymptotic normality in partially observed diffusions with small noise : application to FDI, in: Workshop on Stochastic Theory and Control, University of Kansas 2001. In honor of Tyrone E. Duncan on the occasion of his 60th birthday, Berlin, B. Pasik–Duncan (editor), Lecture Notes in Control and Information Sciences, Springer–Verlag, Berlin, 2002, no 280, p. 267–282.
  • 66J. S. Liu.

    Monte Carlo strategies in scientific computing, Springer Series in Statistics, Springer–Verlag, New York, 2001.
  • 67B. Ristić, M. S. Arulampalam, N. J. Gordon.

    Beyond the Kalman Filter : Particle Filters for Tracking Applications, Artech House, Norwood, MA, 2004.
  • 68R. Y. Rubinstein, D. P. Kroese.

    The cross–entropy method. A unified approach to combinatorial optimization, Monte Carlo simulation and machine learning, Information Science and Statistics, Springer–Verlag, New York, 2004.
  • 69C. J. Stone.

    Consistent nonparametric regression (with discussion), in: The Annals of Statistics, July 1977, vol. 5, no 4, p. 595–645.
  • 70A. G. Tartakovsky, V. V. Veeravalli.

    General asymptotic Bayesian theory of quickest change detection, in: Theory of Probability and its Applications, 2005, vol. 49, no 3, p. 458–497.

    http://dx.doi.org/10.1137/S0040585X97981202
  • 71S. Thrun, W. Burgard, D. Fox.

    Probabilistic robotics, Intelligent Robotics and Autonomous Agents, The MIT Press, Cambridge, MA, 2005.
  • 72L. Tierney, R. E. Kass, J. B. Kadane.

    Fully exponential Laplace approximations to expectations and variances of nonpositive functions, in: Journal of the American Statistical Association, September 1989, vol. 84, no 407, p. 710–716.

    http://www.jstor.org/stable/2289652
  • 73V.–D. Tran.

    Assimilation de données : les propriétés asymptotiques du filtre de Kalman d'ensemble, Université de Bretagne Sud, Vannes, June 2009.

    http://tel.archives-ouvertes.fr/tel-00412447/fr/
  • 74A. W. van der Vaart, J. A. Wellner.

    Weak convergence and empirical processes, Springer Series in Statistics, Springer–Verlag, Berlin, 1996.


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