Pierre Vandekerkhove

Maitre de Conférences  HDR [fichier.pdf]
Curriculum Vitae (avril 2015) [fichier.pdf]

Currently Guest Professor at the
Georgia Institute of Technology,
School of Aerospace Engineering
270 Ferst Drive Atlanta GA 30332-0150

E-mail : pierre.vandek@univ-mlv.fr

Areas of Interest : Biostatistics, Mathematical Statistics, Applied Probability

Aerospace science : queueing, source decomposition, Graphs, big data.

Material science : extreme value, plasticity phase, heterogeneity.

Biostatistics: epidemiology, microarrays, radiotherapy, EEG signals.

Missing data models: semiparametric mixture models, Hidden Markov Models.

Monte-Carlo methods: convergence rate optimization, control of convergence.

Stochastic Algorithms: EM, Robbins Monro, simulated annealing, two armed bandit algorithm.

Past event : Statistics and Modeling for Complex Data, June 22nd-24th 2011.
Coordinator: Pierre Vandekerkhove. Co-organizers: Arnak Dalalyan and Cristina Butucea.
Past event : Mathematical meeting Bézout-GeorgiaTech, december 18th 2013.
Coordinator: Pierre Vandekerkhove. Co-organizers: Christian Houdré, Karim Lounici.
Past event : SESO 2014, Statistical workshop for smart Energies, June 27th 2014.
Coordinator: Michel de Lara. Co-organizer: Pierre Vandekerkhove.
Forthcoming event : SESO 2015, Statistical workshop for smart Energies, June 26th 2015. Special Labex Bézout meeting.
Coordinator: Michel de Lara. Co-organizer: Pierre Vandekerkhove.

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. (2015). Semiparametric topographical mixture models with symmetric errors. Accepted in Bernoulli [fichier.pdf]

Submitted paper

[1] D. Chauveau D. and P. Vandekerkhove. (2015) The Nearest Neighbor entropy estimate: an adequate tool for high dimensional adaptive MCMC evaluation.

Works in progress

[1] D. Pommeret, P. Vandekerkhove. (2014) . False discovery parametric modelling test
[2] D. Chauveau, E. Feron, A. Marzuoli, Y. Tu, P. Vandekerkhove. (2014). Desourcing causes of flight delays: a new mixture based approach.
[3] H. Holzmann, H. Werner and P. Vandekerkhove. (2014). Semiparametric topographical Mixture model with one known component.