Bibliography

Major publications by the team in recent years

[1]

F. Campillo, Y. A. Kutoyants, F. Le Gland.
Small noise asymptotics of the GLR test for off–line change detection in misspecified diffusion processes, in: Stochastics and Stochastics Reports, 2000, vol. 70, n^o 1–2, p. 109–129.

[2]

F. Cérou.
Long time behaviour for some dynamical noise–free nonlinear filtering problems, in: SIAM Journal on Control and Optimization, 2000, vol. 38, n^o 4, p. 1086–1101.

[3]

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

[4]

F. 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.

[5]

F. 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.

[6]

M. 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.

[7]

F. 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.

[8]

F. 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.

[9]

F. 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.

[10]

C. 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, 2001, chap. 12, p. 247–271.

Publications of the year

Doctoral Dissertations and Habilitation Theses

[11]

V.–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/, Ph. D. Thesis.

Articles in International Peer-Reviewed Journal

[12]

G. Biau, F. Cérou, A. Guyader.
On the rate of convergence of the bagged nearest neighbor estimate, in: Journal of Machine Learning Research, 2010, to appear.

[13]

G. Biau, F. Cérou, A. Guyader.
Rates of convergence of the functional k –nearest neighbor estimator, in: IEEE Transactions on Information Theory, 2010, to appear.

[14]

F. 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, 2010, to appear.

International Peer-Reviewed Conference/Proceedings

[15]

É. Arnaud, F. Le Gland.
SMC with adaptive resampling : Large sample asymptotics, in: Proceedings of the 2009 Workshop on Statistical Signal Processing, Cardiff 2009, IEEE–SPS, September 2009, p. 481–484.

[16]

P. 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.

[17]

F. Cérou, T. Furon, A. Guyader, C. Jégourel.
Estimating the probability of false alarm for a zero–bit watermarking technique, in: 16th International Conference on Digital Signal Processing, Santorin 2009, July 2009.

[18]

A. Ickowicz, J.–P. Le Cadre.
Bi–target tracking within a binary sensor network, in: Proceedings of the 12th International Conference on Information Fusion, Seattle 2009, ISIF, July 2009.

[19]

D. 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.

National Peer-Reviewed Conference/Proceedings

[20]

A. Ickowicz, J.–P. Le Cadre.
Suivi de cibles à l'aide d'un réseau de capteurs, in: Actes du Colloque GRETSI, Dijon 2009, September 2009.

Scientific Books (or Scientific Book chapters)

[21]

T. Furon, L. Pérez–Freire, A. Guyader, F. Cérou.
Estimating the minimal length of Tardos codes, in: 11th International Workshop on Information Hiding, Darmstadt 2009, S. Katzenbeisser, A.–R. Sadeghi (editors), Lecture Notes in Computer Science, Springer, June 2009, n^o 5806, p. 176–190.

[22]

P. L'Écuyer, F. Le Gland, P. Lezaud, B. Tuffin.
Splitting methods, in: Monte Carlo Methods for Rare Event Analysis, Chichester, G. Rubino, B. Tuffin (editors), John Wiley & Sons, 2009, chap. 3, p. 39–61.

[23]

F. Le Gland, V. Monbet, V.–D. Tran.
Large sample asymptotics for the ensemble Kalman filter, in: Handbook on Nonlinear Filtering, Oxford, D. O. Crisan, B. L. Rozovskii (editors), Oxford University Press, 2010, to appear.

Internal Reports

[24]

G. Biau, F. Cérou, A. Guyader.
On the rate of convergence of the bagged nearest neighbor estimate, INRIA, February 2009, n^o 6860
http://hal.inria.fr/inria-00363875/en/, Rapport de Recherche.

[25]

G. Biau, F. Cérou, A. Guyader.
On the rate of convergence of the functional k -NN estimates, INRIA, February 2009, n^o 6861
http://hal.inria.fr/inria-00364555/en/, Rapport de Recherche.

[26]

F. Cérou, P. Del Moral, T. Furon, A. Guyader.
Rare event simulation for a static distribution, INRIA, January 2009, n^o 6792
http://hal.inria.fr/inria-00350762/en/, Rapport de Recherche.

[27]

F. Le Gland, V. Monbet, V.–D. Tran.
Large sample asymptotics for the ensemble Kalman filter, INRIA, August 2009, n^o 7014
http://hal.inria.fr/inria-00409060/en/, Rapport de Recherche.

References in notes

[28]

A. 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.

[29]

M. 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.

[30]

H. 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, 2009, chap. 9, p. 193–214.

[31]

O. 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.

[32]

O. Cappé, É. Moulines, T. Rydén.
Inference in Hidden Markov Models, Springer Series in Statistics, Springer–Verlag, New York, 2005.

[33]

T. M. Cover, P. E. Hart.
Nearest neighbor pattern classification, in: IEEE Transactions on Information Theory, January 1995, vol. IT–13, n^o 1, p. 21–27.

[34]

P.–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.

[35]

P. Del Moral.
Feynman–Kac Formulae. Genealogical and Interacting Particle Systems with Applications, Probability and its Applications, Springer–Verlag, New York, 2004.

[36]

P. 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.

[37]

P. 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, 2000, vol. 1729, p. 1–145.

[38]

R. Douc, A. Guillin, J. Najim.
Moderate deviations for particle filtering, in: The Annals of Applied Probability, February 2005, vol. 15, n^o 1B, p. 587–614.

[39]

R. 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.

[40]

A. Doucet, S. J. Godsill, C. Andrieu.
On sequential Monte Carlo sampling methods for Bayesian filtering, in: Statistics and Computing, July 2000, vol. 10, n^o 3, p. 197–208.

[41]

E. Fix, J. L. Hodges.
Discriminatory analysis. Nonparametric discrimination : Consistency properties, USAF School of Aviation Medicine, Randolph Field, February 1951, n^o 4, Project Number 21–49–004.

[42]

E. Fix, J. L. Hodges.
Discriminatory analysis : Small sample performance, USAF School of Aviation Medicine, Randolph Field, August 1952, n^o 11, Project Number 21–49–004.

[43]

D. 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.

[44]

D. 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.

[45]

D. 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.

[46]

D. 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, 2001, chap. 19, p. 401–428.

[47]

D. Frenkel, B. Smit.
Understanding Molecular Simulation. From Algorithms to Applications, Computational Science Series, 2nd, Academic Press, San Diego, 2002, vol. 1.

[48]

P. Glasserman.
Monte Carlo Methods in Financial Engineering, Applications of Mathematics, Springer–Verlag, New York, 2004, vol. 53.

[49]

P. 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.

[50]

P. Glasserman, W. Kang, P. Shahabuddin.
Fast simulation of multifactor portfolio credit risk, in: Operations Research, September–October 2008, vol. 56, n^o 5, p. 1200–1217.

[51]

N. 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.

[52]

F. 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.

[53]

M. 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.

[54]

M. 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.

[55]

G. 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.

[56]

A. N. Kolmogorov, V. M. Tikhomirov.
–entropy and –capacity of sets in functional spaces, in: Twelve papers on Algebra and Real Functions, Providence, AMS Translations — Series 2, AMS, 1961, vol. 17, p. 277–364.

[57]

S. R. Kulkarni, S. E. Posner.
Rates of convergence of nearest neighbor estimation under arbitrary sampling, in: IEEE Transactions on Information Theory, July 1995, vol. IT–41, n^o 4, p. 1028–1039.

[58]

H. 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.

[59]

Y. A. Kutoyants.
Identification of Dynamical Systems with Small Noise, Mathematics and its Applications, Kluwer Academic Publisher, Dordrecht, 1994, vol. 300.

[60]

P. 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.

[61]

L. Le Cam.
Asymptotic Methods in Statistical Decision Theory, Springer Series in Statistics, Springer–Verlag, New York, 1986.

[62]

F. Le Gland.
Combined use of importance weights and resampling weights in sequential Monte Carlo methods, in: ESAIM : Proceedings, 2007, vol. 19, p. 85–100 (electronic).

[63]

F. 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, 2006, n^o 337, p. 351–389.

[64]

F. 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, 2002, n^o 280, p. 267–282.

[65]

L. 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.

[66]

J. S. Liu.
Monte Carlo Strategies in Scientific Computing, Springer Series in Statistics, Springer–Verlag, New York, 2001.

[67]

D. Preiss.
Gaussian measures and the density theorem, in: Commentationes MathematicæUniversitatis Carolinæ, 1981, vol. 22, n^o 1, p. 181–193.

[68]

B. Ristić, M. S. Arulampalam, N. J. Gordon.
Beyond the Kalman Filter : Particle Filters for Tracking Applications, Artech House, Norwood, MA, 2004.

[69]

R. 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.

[70]

T. Schön, F. Gustafsson, P.–J. Nordlund.
Marginalized particle filters for mixed linear / nonlinear state–space models, in: IEEE Transactions on Signal Processing, July 2005, vol. SP–53, n^o 7, p. 2279–2289.

[71]

C. J. Stone.
Consistent nonparametric regression (with discussion), in: The Annals of Statistics, July 1977, vol. 5, n^o 4, p. 595–645.

[72]

S. Thrun, W. Burgard, D. Fox.
Probabilistic Robotics, Intelligent Robotics and Autonomous Agents, The MIT Press, Cambridge, MA, 2005.

[73]

A. W. van der Vaart, J. A. Wellner.
Weak Convergence and Empirical Processes, Springer Series in Statistics, Springer–Verlag, Berlin, 1996.