Mathematical Methods of Modern Statistics
July 10 – 14, 2017

Scientific Committee

Małgorzata Bogdan  (Wrocław University)
Emmanuel Candes (Stanford University)
Hélène Massam (York University, Toronto)
Pascal Massart (Université Paris-Sud)
Judith Rousseau (Université Paris-Dauphine)

Organizing Committee

Piotr Graczyk (Université d’Angers)
Fabien Panloup (Université d’Angers)
Frédéric Proïa (Université d’Angers)
Etienne Roquain (Université Pierre et Marie Curie)
Jacek Wesołowski (Warsaw University of Technology)


Harald Cramér’s  Mathematical Methods of Statistics is a landmark both for Mathematics and Statistics.  2016  was the 70th anniversary of its first publication. Again, after 70 years, mathematics and statistics seem to be falling more and more apart. Numerous statistical procedures are based on ad hoc methods supported by intensive computer-assisted simulations, while mathematicians and mathematical statisticians not always know, face  and confront the real issues of modern statistics.  A much better understanding between statisticians and mathematicians  is essential for the development of both these fields: a new Mathematical Methods of Modern Statistics remains to be written. 

Our aim is to gather in Luminy world class statisticians who use excellent mathematics, and mathematicians who work in their area of interest. We plan to discuss recent achievements in modern statistics, requiring an extensive use of deep mathematical methods and models. We would like to put emphasis on detailed exposition of mathematics  behind the tools used in statistics.

The major unifying topic will be the analysis of large dimensional  data.
The conference will include the following topics:

1. methods of multiple testing      
2. model selection theory                 
3. notions of model sparsity           
4. regularization techniques             
5. missing data treatments             
6. hierarchical and graphical models
7. modern non parametric Bayes methods (BNP)
8. interactions between the above topics
9. random matrices
10. mathematical methods applied in the above topics

The subject of the conference will be viewed from:

(a) the frequentist perspective
(b) the Bayesian perspective


Felix Abramovich (Tel Aviv University)   From model selection in GLM to sparse logistic classification   (pdf)
Julyan Arbel (Inria Grenoble Rhône-Alpes)   Investigating predictive probabilities of Gibbs-type priors   (pdf)
​Sylvain Arlot (Université Paris-Sud)   Analysis of some purely random forests  (pdf)
Yannick Baraud (Université de Nice-Sophia-Antipolis)   How to make Bayes estimators robust
Jean-Marc Bardet (Université Paris 1)   Statistical analysis of causal affine processes   (pdf)
Yoav Benjamini (Tel Aviv University)   A review of challenges in high dimensional multiple inferences   (pdf)
Philippe Biane (Université Paris Est)   Free probability and random matrices   (pdf)
Lucien Birgé   (Université Pierre-et-Marie-Curie)  How to make Bayes estimators robust
Małgorzata Bogdan (Wrocław University)    Sorted L-One Penalized Estimation   (pdf)
Thomas Bonis (Telecom Paristech) Density estimation from k-nn graphs   (pdf)
Włodzimierz Bryc  (University of Cincinnati)   Cauchy-Stieltjes families with polynomial variance functions   (pdf)
Emmanuel Candès (Stanford University)   A new read of the knockos framework : new statistical tools for replicable selections
Ismael Castillo (Université Pierre-et-Marie-Curie)   Uniform estimation of some random graph parameters   (pdf)
Aymeric Dieuleveut (ENS Paris)   Bridging the gap between Stochastic Approximation and Markov chains   (pdf)
​Mathias Drton (University of Washington, Seattle)   Regularized score matching for graphical models : Non-Gaussianity and missing data   (pdf)
David Dunson  (Duke University)   Bayesian manifold learning   (pdf)
Christophe Giraud (Université Paris Sud)   Clustering with convex optimisation   (pdf)
Svetlana Gribkova (Université Paris Diderot)  ZINB-WaVE: dimension reduction and signal extraction for zero-inflated count data analysis   (pdf)
Ruth Heller (Tel Aviv University)   Inference Following Aggregate Level Hypothesis Testing  (pdf)
Hideyuki Ishi (Nagoya University)   Wishart laws for a wide class of regular convex cones   (pdf)
Julie Josse (Ecole polytechnique)   Inference with missing values using principal components methods   (pdf)
Guillaume Kon Kam King (University of Torino) Bayesian Nonparametric functional forecasting with locally-autoregressive particle systems  (pdf)
Rafał Latała (Warsaw University)   Comparison of weak and strong moments for vectors with independent coordinates   (pdf)
Steffen Lauritzen (University of Copenhagen)   Maximum likelihood estimation of totally positive Gaussian distributions   (pdf)
MichaLemanczyk (Warsaw University)  Bernstein-like inequality for Markov chains   (pdf)
Oleg Lepski (Université d’Aix-Marseille)   Estimation in the convolution structure density model   (pdf)
Gérard Letac (Université de Toulouse)   A generalisation of the Sabot-Tarrès integral and the multivariate normal law with non positive correlations
Clément Marteau (Université Claude Bernard, Lyon)   Parameter recovery in two-component contamination mixtures​: the L2 strategy   (pdf)
Hélène Massam (York University, Toronto)  The maximum likelihood estimate in high-dimensional discrete graphical models   (pdf)
Nicolai Meinshausen (ETH Zürich)   Causal Dantzig : fast inference in linear structural equation models   (pdf)
​Takaaki Nomura ( Kyushu University, Fukuoka)   Homogeneous open convex cones : recent results   (pdf)
Yann Ollivier (Université Paris-Sud)   Real-time gradient descents for learning dynamical systems
Dominique Picard (Université Paris Diderot)   Clustering high dimensional data   (pdf)
​Agnieszka Piliszek (Warsaw University ofTechnology)   Message hidden in the Independence of MatrixKummer and Wishart Matrices   (pdf)
​Wojciech Rejchel (Nicolaus Copernicus University)  Penalized Monte Carlo methods in high-dimensional Ising model   (pdf)
Geneviève Robin (Ecole polytechnique) Low-rank Interaction Contingency Tables   (pdf)
Etienne Roquain (Université Pierre-et-Marie-Curie)   Post hoc inference via JER control   (pdf)
Judith Rousseau (Université Paris Dauphine)   Bayesian nonparametric inference for multivariate Hawkes processes   (pdf)
Chiara Sabatti (Stanford University)    Selective inference in genetics   (pdf)
Richard Samworth (Cambridge University)   Efficient multivariate entropy estimation via k-nearest neighbour distances   (pdf)
David Siegmund (Stanford University)  Detection and estimation of local signals   (pdf)
Jonathan Taylor (Stanford University)   Inferactive data analysis
Surya Tokdar (Duke University)   Joint Estimation of Quantile Planes   (pdf)
Sara Van de Geer (ETH Zürich)   Estimating equations and sharp oracle results   (pdf)
Aad Van der Vaart  (Leiden University)   Statistical estimation of a network model
Jean Phillippe Vert (ENS Ulm, Paris)   Learning on the symmetric group   (pdf)
Nicolas Verzelen (INRA Montpellier)   On graphon estimation   (pdf)
Jacek Wesołowski (Warsaw University)   Morality and immorality for discrete graphical models   (pdf)
Daniel Yekutieli (Tel Aviv University)    Confidence Intervals for the CDF from « noisy » iid samples   (pdf)