Nonparametric Statistics

This volume presents the latest advances and trends in nonparametric statistics, and gathers selected and peer-reviewed contributions from the 3rd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Avignon, France on June 11-16, 2016. It covers a broad range of nonparametric statistical methods, from density estimation, survey sampling, resampling methods, kernel methods and extreme values, to statistical learning and classification, both in the standard i.i.d. case and for dependent data, including big data. 

The International Society for Nonparametric Statistics is uniquely global, and its international conferences are intended to foster the exchange of ideas and the latest advances among researchers from around the world, in cooperation with established statistical societies such as the Institute of Mathematical Statistics, the Bernoulli Society and the International Statistical Institute. The 3rd ISNPS conference in Avignonattracted more than 400 researchers from around the globe, and contributed to the further development and dissemination of nonparametric statistics knowledge.

• Nonparametric statistics
• Time series
• Statistical learning
• Kernel methods
• Resampling
• Big data
• Survey sampling
• Nonparametric smoother
• Heavy-tailed distribution
• Dependent data
• Machine learning
• High-dimensional data
• Nonparametric inference

• MODAL’X, Paris West University Nanterre La Défense, Nanterre, France

  Patrice Bertail

• LMA, Avignon University, Avignon, France

  Delphine Blanke

• MIASHS, University of Rennes 2, Rennes, France

  Pierre-André Cornillon

• Formation Continue CEPE, Ecole Nationale de la Statistique et de l’Administration, Malakoff, France

  Eric Matzner-Løber

Patrice Bertail is a Professor of Statistics at the University Paris-Nanterre, France, and member of the chair of Big Data at TelecomParisTech. The author of over 100 peer-reviewed papers, he is a specialist in resampling methods for dependent data. His research interests also include statistical inference for Markov chains and survey sampling for big data. The chief applications of his work are in food risk assessments and insurance models. 

Eric Matzner-Lober is a Professor of Statistics at the University of Rennes 2, France, and an associated member of the National Laboratory of Los Alamos, USA. He is currently in charge of adult formations in statistics at ENSAE. The author of several papers on nonparametric statistics and numerous books on statistics with R, Matzner-Lober is also actively involved in research programs with companies.

Pierre-André Cornillon is an Assistant Professor of Statistics at Rennes University, France, anda member of IRMAR. He is primarily interested in nonparametric regression and applications in R, and he has developed R packages and written several publications, including two books, on these topics. Together with Eric Matzner-Lober, Cornillon is a director of Pratique R, a book collection devoted to applied statistics with R.

Delphine Blanke has been a Professor of Statistics at Avignon University, France, since 2008. Her main research fields are asymptotic statistics, functional estimation and statistical inference for stochastic processes. She is the author of over thirty peer-reviewed papers and one book on nonparametric estimation, prediction, and theory of linear processes in function spaces.

Policies and ethics