Pierre Vandekerkhove
International correspondant (INSMI, CNRS). Lettres de l'INSMI
Curriculum Vitae (2024) [fichier.pdf]
Université Gustave Eiffel
5 Boulevard Descartes,
77420 Champs-sur-Marne
France
E-mail : pierre.vandekerkhove@univ-eiffel.fr
Areas of Interest : Applied and Mathematical Statistics, Applied Probability, Machine Learning
Missing data models: Semiparametric Mixture Models, Multi-Instance Learning, Hidden Markov Models.
Monte-Carlo methods: Entropy, high dimension, filamentary distribution, convergence rate optimization, control of convergence.
Stochastic Algorithms: EM, Robbins Monro, simulated annealing, two armed bandit algorithm.
Applications: Machine learning for climate change, insurance, fraud detection, astronomy, material science, epidemiology, microarrays, radiotherapy, EEG signals.
Funded projects:
[1] Member of the Chaire of Excellence: ACTIONS (1.65 Mio Euro). Partners: BNP Paribas CARDIF, Institut Louis Bachelier et institut des Actuaires.
About the project and team.
[2] Member of the IMPT project: rODEo (Order and Disorder in a Turbulent Ocean) (73 k Euro). IMPT: Institut des Mathématiques pour la Planète Terre (INSMI, CNRS).
About the project and team.
Organized conferences:
[1] Journées de Statistique à Marne-la-Vallée, January 2005.
Co-organizers: Laurent Bordes, Didier Chauveau and Pierre Vandekerkhove.
[2] International conference of Statistics, Pau June 2008.
Coordinator: Laurent Bordes. Co-organizers: Didier Chauveau and Pierre Vandekerkhove.
[3]Statistics and Modeling for Complex Data, June 22nd-24th 2011.
Coordinator: Pierre Vandekerkhove. Co-organizers: Arnak Dalalyan and Cristina Butucea.
[4] Mathematical
meeting Bézout-GeorgiaTech, december 18th 2013.
Coordinator: Pierre Vandekerkhove. Co-organizers: Christian Houdré, Karim Lounici.
[5] SESO 2014, Statistical workshop for smart Energies,
June 27th 2014.
Coordinator: Michel de Lara. Co-organizer: Pierre Vandekerkhove.
[6] SESO 2015, Statistical workshop for smart Energies,
June 26th 2015. Special Labex Bézout meeting.
Coordinator: Michel de Lara. Co-organizer: Pierre Vandekerkhove.
[7] MHC2021 conference (Mixtures, Hidden Markov Models, Clustering),
June 2-4th 2021, Institut mathématique d'Orsay.
Organizers: Elisabeth Gassiat, Hajo Holzmann and Pierre Vandekerkhove.
Poster with keynote speakers list can be dowloaded here [fichier.pdf]
Published papers
[1] D. Bakry, X. Milhaud, P. Vandekerkhove. (1997) Statistics of Hidden Markov chains with
finite state space. The nonstationary case. C. R. Acad. Sci. Paris Série. I, p. 203-206.
[fichier.pdf]
[2] P. Vandekerkhove. (1998) Simulated annealing with a sequential estimator of the
energy. C.R. Acad. Sci. Paris, t. 329, Série I, p.1003-1006.
[fichier.pdf]
[3] D. Chauveau, P. Vandekerkhove. (1999) Un algorithme de Hastings-Metropolis avec
apprentissage séquentiel. C.R. Acad. Sci. Paris, t. 329, Série I, p.173-176.
[fichier.pdf]
[4] P. Giudici, T. Rydén, P. Vandekerkhove. (2000) Likelihood-Ratio Tests for
Hidden Markov Models. Biometrics, 56, p.742-747.
[fichier.pdf]
[5] D. Chauveau, P. Vandekerkhove. (2001) Algorithmes de Hastings Metropolis
en interaction. C.R. Acad. Sci. Paris, t. 333, Série I, p.881-884.
[6] D. Chauveau, P. Vandekerkhove. (2002) Improving convergence of the
Hastings-Metropolis Algorithm with a learning proposal. Scand. J. Statist, 28, p.13-29.
[fichier.pdf]
[7] P. Vandekerkhove. (2005) Consistent and asymptotically normally distributed estimates
for Hidden
Markov Mixtures of Markov Models. Bernoulli, 11, p.103-129.
[fichier.pdf]
[8] L. Bordes, P. Vandekerkhove. (2005) Statistical inference for partially Hidden Markov Models.
Communication in Statistics, Theory and Method, 34, p.1081-1104.
[fichier.pdf]
[9] L. Bordes, S. Mottelet., P. Vandekerkhove. (2006) Semiparametric estimation of a two components
mixture model. Annals of Statistics, 34, p.1204-1232.[fichier.pdf]
[10] L. Bordes, C. Delmas, P. Vandekerkhove. (2006) Semiparametric estimation of a two-component
mixture model when a component is known. Scandinavian Journal of Statistics, 33, p. 733-752.[fichier.pdf]
[11] L. Bordes, D. Chauveau, P. Vandekerkhove. (2007) Semiparametric EM algorithm for a two-component
mixture model.
Computational Statistics and Data Analysis, 51, p. 5429-5443.
[fichier.pdf]
[12] D. Chauveau, P. Vandekerkhove. (2007) A Monte Carlo estimation of the entropy for Markov chains.
Methodology and Computing in Applied Probability, 9, p.133-149.
[fichier.pdf]
[13] L. Bordes, P. Vandekerkhove. (2010) . Semiparametric two-component mixture model
when a component is known: an asymptotically normal estimator.
Mathematical Methods of Statistics, 19, p. 22-41.[fichier.pdf]
[14] P. Tarrès, P. Vandekerkhove. (2012) On ergodic two-armed bandits. Annals of Applied
Probability, 22, p. 457-476. [fichier.pdf]
[15] G. Fort, E. Moulines, P. Priouret, P. Vandekerkhove. (2012) A simple variance inequality
for U-statistics of a Markov chain with applications. Statistics and Probability letters, 82, p. 1193-1201. [fichier.pdf]
[16] D. Chauveau, P. Vandekerkhove. (2013) Smoothness of Metropolis-Hastings algorithm and application to entropy estimation.
ESAIM P&S, 17, 419-431. [fichier.pdf]
[17] P. Vandekerkhove. (2013) Estimation of a semiparametric mixture of regressions model. Journal of
Nonparametric Statistics, 25, 181-208.
[fichier.pdf]
[18] L. Bordes, I. Kojadinovic, P. Vandekerkhove. (2013) Semiparametric estimation of a mixture
of two linear regressions where one component is known. Electronic Journal of Statistics, p. 2603-2644.
[fichier.pdf]
[19] D. Chauveau D. and P. Vandekerkhove. (2014)
Simulation Based Nearest Neighbor Entropy Estimation
for (Adaptive) MCMC Evaluation, JSM Proceedings, Statistical Computing Section. Alexandria, VA:
American Statistical Association, p. 2816-2827. [fichier.pdf]
[20] C. Butucea, P. Vandekerkhove. (2014) Semiparametric mixtures of symmetric distributions. Scandinavian
Journal of Statistics, 41, p. 227-239. [fichier.pdf]
[21] G. Fort, E. Moulines, P. Priouret, P. Vandekerkhove. (2014) A central limit Theorem for adaptive and interacting
Markov chains. Bernoulli, 20, p. 457-485 [fichier.pdf]
[22] P. Vandekerkhove, J. M. Padbidri, and D. L. McDowell. (2014) Integrated Cumulative Error (ICE) distance for mixture model
selection: Application to extreme values in metal fatigue problems.
Electronic Journal of Statistics, 8, p. 3141-3175. [fichier.pdf]
[23] C. Butucea, R. Nguyepe Zumpe, P. Vandekerkhove. (2017).
Semiparametric topographical mixture models with symmetric errors. Bernoulli, 23, p. 825-862. [fichier.pdf]
[24] D. Pommeret, P. Vandekerkhove. (2019) . Semiparametric density testing in the contamination model.
Electronic Journal of Statistics, 13, p. 4743-4793.
[fichier.pdf]
Supplementary File [fichier.pdf]
[25] H. Werner, H. Holzmann and P. Vandekerkhove. (2019).
Adaptive estimation in the sup-norm for conditional semiparametric mixtures.
Electronic Journal of Statistics, 14, p. 1816 - 1871. [fichier.pdf]
[26] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2022). Semiparametric Two-sample mixture components comparison test.
Journal of Statistical Planning and Inference, 216, p. 135-150.
[fichier.pdf]
[27] F. Maire , P. Vandekerkhove. (2022).
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling. SIAM journal on Mathematics and Data Science, Vol.4.
[fichier.pdf]
[28] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2024).
Two-sample contamination model test. Bernoulli, 30, p. 170 - 197. [fichier.pdf]
See also the admix R package by Xavier Milhaud.
[29] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2024). Contamination-based K-sample clustering.
Journal of Machine Learning Research, 25, p. 1-32. [fichier.pdf]
[30] D. Chauveau D. and P. Vandekerkhove. (2025). The Nearest Neighbor entropy estimate: an
adequate tool for high dimensional adaptive MCMC evaluation. SIAM journal of uncertainty quantification, Vol. 13, Issue 1.
[fichier.pdf]
See also the EntropyMCMC R package by D. Chauveau and H. Alrachid.
[31] T. Garcia, L. Oms, X. Milhaud, A. Doglioli, M. Messié, P.
Vandekerkhove, C. Lacour, G. Grégori, and D. Pommeret. (2026). A statistical approach to unveil phytoplankton adaptation to ocean
fronts. Advances in Statistical Climatology, Meteorology and Oceanography, Vol. 12, p.21-41. [fichier.pdf]
Submitted papers
[1] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2025). admix: An R Package for Estimation, Test and Clustering in Admixture Models.
[fichier.pdf]
[2] C. Lacour and P. Vandekerkhove. (2025). Gold standard process Markovian poisoning: a semiparametric approach.
[fichier.pdf]
Works in progress
[1] X. Milhaud, O. Lopez and P. Vandekerkhove. (2025). Contamination model independence test.
[2] R. Worms and P. Vandekerkhove. (2026). Extremes of a contaminant distribution in the background of a gold standard
Organized conferences:
[1] Journées de Statistique à Marne-la-Vallée, January 2005.
Co-organizers: Laurent Bordes, Didier Chauveau and Pierre Vandekerkhove.
[2] International conference of Statistics, Pau June 2008.
Coordinator: Laurent Bordes. Co-organizers: Didier Chauveau and Pierre Vandekerkhove.
[3]Statistics and Modeling for Complex Data, June 22nd-24th 2011.
Coordinator: Pierre Vandekerkhove. Co-organizers: Arnak Dalalyan and Cristina Butucea.
[4] Mathematical meeting Bézout-GeorgiaTech, december 18th 2013.
Coordinator: Pierre Vandekerkhove. Co-organizers: Christian Houdré, Karim Lounici.
[5] SESO 2014, Statistical workshop for smart Energies, June 27th 2014.
Coordinator: Michel de Lara. Co-organizer: Pierre Vandekerkhove.
[6] SESO 2015, Statistical workshop for smart Energies, June 26th 2015. Special Labex Bézout meeting.
Coordinator: Michel de Lara. Co-organizer: Pierre Vandekerkhove.
[7] MHC2021 conference (Mixtures, Hidden Markov Models, Clustering), June 2-4th 2021, Institut mathématique d'Orsay.
Organizers: Elisabeth Gassiat, Hajo Holzmann and Pierre Vandekerkhove.
Poster with keynote speakers list can be dowloaded here [fichier.pdf]
Published papers
[1] D. Bakry, X. Milhaud, P. Vandekerkhove. (1997) Statistics of Hidden Markov chains with
finite state space. The nonstationary case. C. R. Acad. Sci. Paris Série. I, p. 203-206.
[fichier.pdf]
[2] P. Vandekerkhove. (1998) Simulated annealing with a sequential estimator of the
energy. C.R. Acad. Sci. Paris, t. 329, Série I, p.1003-1006.
[fichier.pdf]
[3] D. Chauveau, P. Vandekerkhove. (1999) Un algorithme de Hastings-Metropolis avec
apprentissage séquentiel. C.R. Acad. Sci. Paris, t. 329, Série I, p.173-176.
[fichier.pdf]
[4] P. Giudici, T. Rydén, P. Vandekerkhove. (2000) Likelihood-Ratio Tests for
Hidden Markov Models. Biometrics, 56, p.742-747.
[fichier.pdf]
[5] D. Chauveau, P. Vandekerkhove. (2001) Algorithmes de Hastings Metropolis
en interaction. C.R. Acad. Sci. Paris, t. 333, Série I, p.881-884.
[6] D. Chauveau, P. Vandekerkhove. (2002) Improving convergence of the
Hastings-Metropolis Algorithm with a learning proposal. Scand. J. Statist, 28, p.13-29.
[fichier.pdf]
[7] P. Vandekerkhove. (2005) Consistent and asymptotically normally distributed estimates
for Hidden
Markov Mixtures of Markov Models. Bernoulli, 11, p.103-129.
[fichier.pdf]
[8] L. Bordes, P. Vandekerkhove. (2005) Statistical inference for partially Hidden Markov Models.
Communication in Statistics, Theory and Method, 34, p.1081-1104.
[fichier.pdf]
[9] L. Bordes, S. Mottelet., P. Vandekerkhove. (2006) Semiparametric estimation of a two components
mixture model. Annals of Statistics, 34, p.1204-1232.[fichier.pdf]
[10] L. Bordes, C. Delmas, P. Vandekerkhove. (2006) Semiparametric estimation of a two-component
mixture model when a component is known. Scandinavian Journal of Statistics, 33, p. 733-752.[fichier.pdf]
[11] L. Bordes, D. Chauveau, P. Vandekerkhove. (2007) Semiparametric EM algorithm for a two-component
mixture model.
Computational Statistics and Data Analysis, 51, p. 5429-5443.
[fichier.pdf]
[12] D. Chauveau, P. Vandekerkhove. (2007) A Monte Carlo estimation of the entropy for Markov chains.
Methodology and Computing in Applied Probability, 9, p.133-149.
[fichier.pdf]
[13] L. Bordes, P. Vandekerkhove. (2010) . Semiparametric two-component mixture model
when a component is known: an asymptotically normal estimator.
Mathematical Methods of Statistics, 19, p. 22-41.[fichier.pdf]
[14] P. Tarrès, P. Vandekerkhove. (2012) On ergodic two-armed bandits. Annals of Applied
Probability, 22, p. 457-476. [fichier.pdf]
[15] G. Fort, E. Moulines, P. Priouret, P. Vandekerkhove. (2012) A simple variance inequality
for U-statistics of a Markov chain with applications. Statistics and Probability letters, 82, p. 1193-1201. [fichier.pdf]
[16] D. Chauveau, P. Vandekerkhove. (2013) Smoothness of Metropolis-Hastings algorithm and application to entropy estimation.
ESAIM P&S, 17, 419-431. [fichier.pdf]
[17] P. Vandekerkhove. (2013) Estimation of a semiparametric mixture of regressions model. Journal of
Nonparametric Statistics, 25, 181-208.
[fichier.pdf]
[18] L. Bordes, I. Kojadinovic, P. Vandekerkhove. (2013) Semiparametric estimation of a mixture
of two linear regressions where one component is known. Electronic Journal of Statistics, p. 2603-2644.
[fichier.pdf]
[19] D. Chauveau D. and P. Vandekerkhove. (2014)
Simulation Based Nearest Neighbor Entropy Estimation
for (Adaptive) MCMC Evaluation, JSM Proceedings, Statistical Computing Section. Alexandria, VA:
American Statistical Association, p. 2816-2827. [fichier.pdf]
[20] C. Butucea, P. Vandekerkhove. (2014) Semiparametric mixtures of symmetric distributions. Scandinavian
Journal of Statistics, 41, p. 227-239. [fichier.pdf]
[21] G. Fort, E. Moulines, P. Priouret, P. Vandekerkhove. (2014) A central limit Theorem for adaptive and interacting
Markov chains. Bernoulli, 20, p. 457-485 [fichier.pdf]
[22] P. Vandekerkhove, J. M. Padbidri, and D. L. McDowell. (2014) Integrated Cumulative Error (ICE) distance for mixture model
selection: Application to extreme values in metal fatigue problems.
Electronic Journal of Statistics, 8, p. 3141-3175. [fichier.pdf]
[23] C. Butucea, R. Nguyepe Zumpe, P. Vandekerkhove. (2017).
Semiparametric topographical mixture models with symmetric errors. Bernoulli, 23, p. 825-862. [fichier.pdf]
[24] D. Pommeret, P. Vandekerkhove. (2019) . Semiparametric density testing in the contamination model.
Electronic Journal of Statistics, 13, p. 4743-4793.
[fichier.pdf]
Supplementary File [fichier.pdf]
[25] H. Werner, H. Holzmann and P. Vandekerkhove. (2019).
Adaptive estimation in the sup-norm for conditional semiparametric mixtures.
Electronic Journal of Statistics, 14, p. 1816 - 1871. [fichier.pdf]
[26] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2022). Semiparametric Two-sample mixture components comparison test.
Journal of Statistical Planning and Inference, 216, p. 135-150.
[fichier.pdf]
[27] F. Maire , P. Vandekerkhove. (2022).
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling. SIAM journal on Mathematics and Data Science, Vol.4.
[fichier.pdf]
[28] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2024).
Two-sample contamination model test. Bernoulli, 30, p. 170 - 197. [fichier.pdf]
See also the admix R package by Xavier Milhaud.
[29] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2024). Contamination-based K-sample clustering.
Journal of Machine Learning Research, 25, p. 1-32. [fichier.pdf]
[30] D. Chauveau D. and P. Vandekerkhove. (2025). The Nearest Neighbor entropy estimate: an
adequate tool for high dimensional adaptive MCMC evaluation. SIAM journal of uncertainty quantification, Vol. 13, Issue 1.
[fichier.pdf]
See also the EntropyMCMC R package by D. Chauveau and H. Alrachid.
[31] T. Garcia, L. Oms, X. Milhaud, A. Doglioli, M. Messié, P.
Vandekerkhove, C. Lacour, G. Grégori, and D. Pommeret. (2026). A statistical approach to unveil phytoplankton adaptation to ocean
fronts. Advances in Statistical Climatology, Meteorology and Oceanography, Vol. 12, p.21-41. [fichier.pdf]
Submitted papers
[1] X. Milhaud, D. Pommeret, Y. Salhi and P. Vandekerkhove. (2025). admix: An R Package for Estimation, Test and Clustering in Admixture Models.[fichier.pdf]
[2] C. Lacour and P. Vandekerkhove. (2025). Gold standard process Markovian poisoning: a semiparametric approach. [fichier.pdf]
Works in progress
[1] X. Milhaud, O. Lopez and P. Vandekerkhove. (2025). Contamination model independence test.[2] R. Worms and P. Vandekerkhove. (2026). Extremes of a contaminant distribution in the background of a gold standard