Publications

Preprints and Notes


N. Verzelen. Spectral label recovery for Stochastic block models. 2015. [pdf] (Notes of a short course given at ENSAE in April 2015).
(1) F. Bunea, C. Giraud, M. Royer, and N. Verzelen. PECOK: a convex optimization approach to variable clustering.[Arxiv]
(2) N. Verzelen and E. Gassiat. Adaptive estimation of High-Dimensional Signal-to-Noise Ratios.[Arxiv]
(3) E. Arias-Castro, G. Lugosi, and N. Verzelen Detecting a Path of Correlations in a Network [Arxiv]
(4) E. Arias-Castro, S. Bubeck, G. Lugosi, and N. Verzelen. Detecting Markov Random Fields Hidden in White Noise [Arxiv]
(5) E. Arias-Castro and N. Verzelen. Detection and Feature Selection in Sparse Mixture Models (Annals of Statistics, accepted) [Arxiv]

Publications

(2016)


(6) E. Arias-Castro and N. Verzelen. Discussion of: Influential features PCA for High-dimensional clustering. Annals of Statistics (to appear)
(7) O. Klopp, A. Tsybakov, and N. Verzelen.  Oracle inequalities for network models and Sparse Graphon estimation. Annals of Statistics (to appear)[Arxiv]

(2015)


(8) C. Charbonnier, N. Verzelen and F. Villers. A Global Homogeneity Test for High-Dimensional Linear Regression. Electronical  Journal of  Statistics Vol. 9, 318–382. 2015. [Arxiv]
(9) E. Arias-Castro and N. Verzelen. Community Detection in Sparse Random Networks. Annals of Applied. Probability . Vol. 25(6), 3465–3510 [Arxiv]
(10) M. Thomas, N. Verzelen, P.barbillon, and 21 authors. A Network-Based Method to Detect Patterns of Local Crop Biodiversity: Validation at the Species and Infra-Species Levels. Advances in Ecological Research, Vol. 53, 259–320

(2014)


(11)
E. Arias-Castro and N. Verzelen. Community Detection in Random Networks. Annals of Statistics Vol. 42(3), 940–969 [Arxiv]

(2013)


(12) N. Hilgert, A. Mas and N. Verzelen. Minimax adaptive tests for the Functional Linear model. Annals of Statistics Vol. 41(2), 838–869 [Arxiv] [Journal]
(13) I. Vilmus, M. Ecarnot, N. Verzelen, and P. Roumet. Monitoring Nitrogen Leaf Resorption Kinetics by Near-Infrared Spectroscopy during Grain Filling in Durum Wheat in Different Nitrogen Availability Condition.  Crop Science , Vol .53, 284–296.

(2012)


(14) C. Giraud, S. Huet and N. Verzelen. High-dimensional regression with unknown variance. Statistical Science, Vol. 27(4) 500–518. [arXiv] [Journal] [Appendix] [Erratum]
(15) N. Verzelen, W. Tao and H. Mueller. Inferring stochastic dynamics from functional data. Biometrika Vol. 99 (3), 533–550. [Preprint] [Journal]
(16) N. Verzelen. Minimax risks for sparse regressions: Ultra-high-dimensional phenomenons. Electronical Journal of Statistics Vol. 6, 38–90. 2012.[pdf][Appendix]
(17) C. Giraud, S. Huet and N. Verzelen. Graph selection with GGMselect. SAGMB, Vol. 11 (3). [arXiv][Journal]

(2010)


(18) Y. Ingster, A. Tsybakov and N. Verzelen. Detection boundary in sparse regression. Electronical Journal of Statistics Vol. 4, 1476–1526. [pdf]
(19) N. Verzelen. Adaptive estimation of covariance matrices via Cholesky decomposition. Electronical Journal of  Statistics Vol. 4, 1113–1150 [pdf] [Appendix]
(20) N. Verzelen. Adaptive estimation of stationary Gaussian fields. Annals of. Statististics Vol. 38(3), 1363–1402. [pdf] [Appendix]
(21) N. Verzelen. Data-driven neighborhood selection of a Gaussian field. Comput. Statist. Data Anal. Vol. 54(5), 1355–1371. [pdf]
(22) N. Verzelen. High-dimensional Gaussian model selection on a Gaussian design. Annales de l’Institut Henri Poincaré (B). Vol. 46(2), 480–524. [pdf]
(23) N. Verzelen and F. Villers. Goodness-of-fit tests for high-dimensonal Gaussian linear models. Annals of Statistics Vol. 38(2), 704–752. [pdf]

(2009)


(24) N. Verzelen and F. Villers. Tests for Gaussian graphical models. Comput. Statist. Data Anal., Vol. 53(5), 1894–1905. [pdf]

(2008)


(25) N. Cressie and N. Verzelen. Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields. Comput. Statist. Data Anal., Vol. 52(5),  2794–2807. [pdf]

(2006)


(26) N. Verzelen, N. Picard, and S. Gourlet-Fleury. Approximating spatial interactions in a model of forest dynamics as a means of understanding spatial patterns. Ecological Complexity, Vol. 3(3), 209–218.

Packages


(1)  GGMselect package for R 2.9.1. [webpage] [Introduction to GGMselect]

Thesis

N. Verzelen. Gaussian Graphical models and model selection. PhD Thesis. Université Paris-Sud. 2008. [pdf]

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