Bibliography

• 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, n^o 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.
  
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• 4F. Cérou, A. Guyader.
  
  Adaptive multilevel splitting for rare event analysis, in: Stochastic Analysis and Applications, March 2007, vol. 25, n^o 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.
  
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• 7F. Le Gland, L. Mevel.
  
  Exponential forgetting and geometric ergodicity in hidden Markov models, in: Mathematics of Control, Signals, and Systems, 2000, vol. 13, n^o 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, n^o 337, p. 351–389.
  
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• 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.

Doctoral Dissertations and Habilitation Theses

• 10A. Guyader.
  
  Contributions à l'estimation non paramétrique et à la simulation d'évènements rares, Université de Rennes 2, December 2011, Habilitation à Diriger des Recherches.

Articles in International Peer-Reviewed Journal

• 11P. Ailliot, A. Baxevani, A. Cuzol, V. Monbet, N. Raillard.
  
  Space–time models for moving fields. Application to significant wave height, in: Environmetrics, May 2011, vol. 22, n^o 3, p. 354–369.
  
  http://dx.doi.org/10.1002/env.1061
• 12P. Ailliot, V. Monbet.
  
  Markov switching autoregressive models for wind time series, in: Environmental Modelling & Software, 2012, to appear.
  
  http://dx.doi.org/10.1016/j.envsoft.2011.10.011
• 13F. Cérou, P. Del Moral, A. Guyader.
  
  A nonasymptotic variance theorem for unnormalized Feynman–Kac particle models, in: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2011, vol. 47, n^o 3, p. 629–649.
  
  http://dx.doi.org/10.1214/10-AIHP358
• 14F. Cérou, P. Del Moral, A. Guyader, T. Furon.
  
  Sequential Monte Carlo for rare event estimation, in: Statistics and Computing, 2012, to appear.
  
  http://dx.doi.org/10.1007/s11222-011-9231-6
• 15F. Cérou, A. Guyader, T. Lelièvre, D. Pommier.
  
  A multiple replica approach to simulate reactive trajectories, in: Journal of Chemical Physics, February 2011, vol. 134, n^o 5.
  
  http://dx.doi.org/10.1063/1.3518708
• 16F. Cérou, A. Guyader, R. Y. Rubinstein, R. Vaisman.
  
  Smoothed splitting method for counting, in: Stochastic Models, 2011, vol. 27, n^o 4, p. 629–650.
  
  http://dx.doi.org/10.1080/15326349.2011.614188
• 17A. Guyader, N. W. Hengartner, E. Matzner-Løber.
  
  Simulation and estimation of extreme quantiles and extreme probabilities, in: Applied Mathematics & Optimization, October 2011, vol. 64, n^o 2, p. 171–196.
  
  http://dx.doi.org/10.1007/s00245-011-9135-z
• 18J. Krystul, F. Le Gland, P. Lezaud.
  
  Sampling per mode for rare event simulation in switching diffusions, in: Stochastic Processes and their Applications, 2012, to appear.
• 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 IMechE, Part O, Journal of Risk and Reliability, 2012, to appear.
  
  http://dx.doi.org/10.1177/1748006X11426973
• 20J. Morio, R. Pastel, F. Le Gland.
  
  Estimation de probabilités et de quantiles rares pour la caractérisation d'une zone de retombée d'un engin, in: Journal de la Société Française de Statistique, 2011, vol. 152, n^o 4.
• 21J. Morio, R. Pastel, F. Le Gland.
  
  Missile target accuracy estimation with importance splitting, in: Aerospace Science and Technology, 2012, to appear.
  
  http://dx.doi.org/10.1016/j.ast.2011.12.006

International Conferences with Proceedings

• 22M. Chouchane, S. Paris, F. Le Gland, C. Musso, D.–T. Pham.
  
  On the probability distribution of a moving target. Asymptotic and non–asymptotic results, in: Proceedings of the 14th International Conference on Information Fusion, Chicago, ISIF, July 2011, p. 99–101.
• 23C. Musso, P. Bui–Quang, F. Le Gland.
  
  Introducing the Laplace approximation in particle filtering, in: Proceedings of the 14th International Conference on Information Fusion, Chicago, ISIF, July 2011, p. 290–297.

National Conferences with Proceeding

• 24M. Bocquel, A. Lepoutre, O. Rabaste, F. Le Gland.
  
  Optimisation d'un filtre particulaire en contexte track–before–detect, in: Actes du 23ème Colloque sur le Traitement du Signal et des Images, Bordeaux 2011, September 2011.

Scientific Books (or Scientific Book chapters)

• 25F. 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.

• 26A. 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.
• 27M. 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, n^o 2 (Special issue on Monte Carlo Methods for Statistical Signal Processing), p. 174–188.
• 28P. Blanchart, L. He, F. Le Gland.
  
  Information fusion for indoor localization, in: Proceedings of the 12th International Conference on Information Fusion, Seattle 2009, ISIF, July 2009, p. 2083–2090.
• 29H. A. P. Blom, B. Bakker, J. Krystul.
  
  Rare event estimation for a large scale stochastic hybrid system with air traffic application, in: Monte Carlo Methods for Rare Event Analysis, Chichester, G. Rubino, B. Tuffin (editors), John Wiley & Sons, Chichester, 2009, chap. 9, p. 193–214.
• 30P. Bui–Quang, C. Musso, F. Le Gland.
  
  An insight into the issue of dimensionality in particle filtering, in: Proceedings of the 13th International Conference on Information Fusion, Edinburgh 2010, ISIF, July 2010, Paper We2.1.2.
• 31O. 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, n^o 5, p. 899–924.
• 32O. Cappé, É. Moulines, T. Rydén.
  
  Inference in hidden Markov models, Springer Series in Statistics, Springer–Verlag, New York, 2005.
• 33P.–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), n^o 1, p. 19–67.
• 34P. Del Moral.
  
  Feynman–Kac formulae. Genealogical and interacting particle systems with applications, Probability and its Applications, Springer–Verlag, New York, 2004.
• 35P. 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, n^o 2, p. 155–194.
• 36P. 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.
• 37R. Douc, C. Matias.
  
  Asymptotics of the maximum likelihood estimator for general hidden Markov models, in: Bernoulli, June 2001, vol. 7, n^o 3, p. 381–420.
• 38G. Evensen.
  
  Ensemble Kalman filter : theoretical formulation and practical implementations, in: Ocean Dynamics, 2003, vol. 53, p. 343–367.
• 39G. Evensen.
  
  Sampling strategies and square root analysis schemes for the EnKF, in: Ocean Dynamics, 2004, vol. 54, p. 539–560.
• 40G. Evensen.
  
  Data assimilation. The ensemble Kalman filter, Springer–Verlag, Berlin, 2006.
• 41D. Fox, W. Burgard, H. Kruppa, S. Thrun.
  
  A probabilistic approach to collaborative multi–robot localization, in: Autonomous Robots, June 2000, vol. 8, n^o 3 (Special issue on Heterogeneous Multi–Robot Systems), p. 325–344.
• 42D. Fox.
  
  Adapting the sample size in particle filters through KLD–sampling, in: International Journal of Robotics Research, December 2003, vol. 22, n^o 12, p. 985–1004.
• 43D. Fox, J. Hightower, L. Liao, D. Schulz, G. Borriello.
  
  Bayesian filtering for location estimation, in: IEEE Pervasive Computing, July/September 2003, vol. 2, n^o 3, p. 24–33.
• 44D. 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.
• 45D. Frenkel, B. Smit.
  
  Understanding molecular simulation. From algorithms to applications, Computational Science Series, 2nd, Academic Press, San Diego, 2002, vol. 1.
• 46P. Glasserman.
  
  Monte Carlo methods in financial engineering, Applications of Mathematics, Springer–Verlag, New York, 2004, vol. 53.
• 47P. Glasserman, P. Heidelberger, P. Shahabuddin, T. Zajic.
  
  Multilevel splitting for estimating rare event probabilities, in: Operations Research, July–August 1999, vol. 47, n^o 4, p. 585–600.
• 48N. 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, n^o 2, p. 107–113.
• 49F. 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, n^o 2 (Special issue on Monte Carlo Methods for Statistical Signal Processing), p. 425–437.
• 50M. Isard, A. Blake.
  
  Condensation — Conditional density propagation for visual tracking, in: International Journal of Computer Vision, August 1998, vol. 29, n^o 1, p. 5–28.
• 51M. R. James, F. Le Gland.
  
  Consistent parameter estimation for partially observed diffusions with small noise, in: Applied Mathematics & Optimization, July/August 1995, vol. 32, n^o 1, p. 47–72.
• 52M. Joannides, F. Le Gland.
  
  Small noise asymptotics of the Bayesian estimator in nonidentifiable models, in: Statistical Inference for Stochastic Processes, 2002, vol. 5, n^o 1, p. 95–130.
• 53G. Kitagawa.
  
  Monte Carlo filter and smoother for non–Gaussian nonlinear state space models, in: Journal of Computational and Graphical Statistics, 1996, vol. 5, n^o 1, p. 1–25.
• 54D. Kubrak, F. Le Gland, L. He, Y. Oster.
  
  Multi–sensor fusion for localization. Concept and simulation results, in: Proceedings of the 2009 ION Conference on Global Navigation Satellite Systems, Savannah 2009, ION, September 2009.
• 55Y. A. Kutoyants.
  
  Identification of dynamical systems with small noise, Mathematics and its Applications, Kluwer Academic Publisher, Dordrecht, 1994, vol. 300.
• 56H. R. Künsch.
  
  Recursive Monte Carlo filters : Algorithms and theoretical analysis, in: The Annals of Statistics, October 2005, vol. 33, n^o 5, p. 1983–2021.
• 57P. 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, n^o 2 (Special issue honoring Perwez Shahabuddin), Article 9.
• 58L. Le Cam.
  
  Asymptotic methods in statistical decision theory, Springer Series in Statistics, Springer–Verlag, New York, 1986.
• 59F. 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, n^o 2, p. 279-316.
• 60F. 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, n^o 1, p. 144–187.
• 61F. 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, n^o 280, p. 267–282.
• 62L. Liao, D. Fox, J. Hightower, H. Kautz, D. Schulz.
  
  Voronoi tracking : Location estimation using sparse and noisy sensor data, in: Proceedings of the IEEE / RSJ International Conference on Intelligent Robots and Systems, Las Vegas 2003, October 2003, p. 723–728.
• 63J. S. Liu.
  
  Monte Carlo strategies in scientific computing, Springer Series in Statistics, Springer–Verlag, New York, 2001.
• 64B. Ristić, M. S. Arulampalam, N. J. Gordon.
  
  Beyond the Kalman Filter : Particle Filters for Tracking Applications, Artech House, Norwood, MA, 2004.
• 65R. 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.
• 66C. J. Stone.
  
  Consistent nonparametric regression (with discussion), in: The Annals of Statistics, July 1977, vol. 5, n^o 4, p. 595–645.
• 67S. Thrun, W. Burgard, D. Fox.
  
  Probabilistic robotics, Intelligent Robotics and Autonomous Agents, The MIT Press, Cambridge, MA, 2005.
• 68V.–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/
• 69P. Zhang.
  
  Nonparametric importance sampling, in: Journal of the American Statistical Association, September 1996, vol. 91, n^o 434, p. 1245–1253.
• 70A. W. van der Vaart, J. A. Wellner.
  
  Weak convergence and empirical processes, Springer Series in Statistics, Springer–Verlag, Berlin, 1996.