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© 2018

Nonparametric Statistics

3rd ISNPS, Avignon, France, June 2016

  • Patrice Bertail
  • Delphine Blanke
  • Pierre-André Cornillon
  • Eric Matzner-Løber
Conference proceedings ISNPS 2016

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 250)

Table of contents

  1. Front Matter
    Pages i-ix
  2. P.-A. Cornillon, A. Gribinski, N. Hengartner, T. Kerdreux, E. Matzner-Løber
    Pages 1-14
  3. N. Hengartner, E. Matzner-Løber, L. Rouvière, T. Burr
    Pages 31-52
  4. A. Carpentier, O. Klopp, M. Löffler
    Pages 103-118
  5. L. Montuelle, E. Le Pennec
    Pages 133-144
  6. D. N. Politis, V. A. Vasiliev, S. E. Vorobeychikov
    Pages 159-169
  7. P. Bertail, O. Jelassi, J. Tressou, M. Zetlaoui
    Pages 185-203

About these proceedings

Introduction

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 Avignon attracted more than 400 researchers from around the globe, and contributed to the further development and dissemination of nonparametric statistics knowledge.

Keywords

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

Editors and affiliations

  • Patrice Bertail
    • 1
  • Delphine Blanke
    • 2
  • Pierre-André Cornillon
    • 3
  • Eric Matzner-Løber
    • 4
  1. 1.MODAL’XParis West University Nanterre La DéfenseNanterreFrance
  2. 2.LMAAvignon UniversityAvignonFrance
  3. 3.MIASHSUniversity of Rennes 2RennesFrance
  4. 4.Formation Continue CEPEEcole Nationale de la Statistique et de l’AdministrationMalakoffFrance

About the editors

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, and a 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.

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