Overview
- Updates the reader on cutting-edge research in nonparametric statistics
- Features the latest findings on high-dimensional data, machine learning, data mining, big data and resampling methods, as well as statistical computing
- Addresses advanced students and researchers alike
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 250)
Included in the following conference series:
Conference proceedings info: ISNPS 2016.
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Table of contents (25 papers)
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Front Matter
Other volumes
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Nonparametric Statistics
Keywords
About this book
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.
Editors and Affiliations
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, 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.
Bibliographic Information
Book Title: Nonparametric Statistics
Book Subtitle: 3rd ISNPS, Avignon, France, June 2016
Editors: Patrice Bertail, Delphine Blanke, Pierre-André Cornillon, Eric Matzner-Løber
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-319-96941-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-96940-4Published: 09 March 2019
eBook ISBN: 978-3-319-96941-1Published: 08 March 2019
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: IX, 390
Number of Illustrations: 27 b/w illustrations, 26 illustrations in colour
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Life Sciences, Medicine, Health Sciences