Nonparametric Statistics
Springer International Publishing (Verlag)
978-3-319-96940-4 (ISBN)
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.
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.
Symmetrizing k -nn and Mutual k -nn Smoothers (P. A. Cornillon, A. Gribinski, N. Hengartner, T. Kerdreux and E. Matzner-Løber).- Multiplicative Bias Corrected Nonparametric Smoothers (N. Hengartner, E. Matzner-Løber, L. Rouvière and T. Burr).- Nonparametric PU Learning of State Estimation in Markov Switching Model (A. Dobrovidov and V. Vasilyev).- Nonparametric Lower Bounds and Information Functions (S. Y. Novak).- Efficiency of the V -fold Model Selection for Localized Bases (F. Navarro and A. Saumard).- Modification of Moment-based Tail Index Estimator: Sums versus Maxima (N. Markovich and M. Vaiciulis).- Constructing Confidence Sets for the Matrix Completion Problem (A. Carpentier, O. Klopp and M. Löffler).- PAC-Bayesian Aggregation of Affine Estimators (L. Montuelle and E. Le Pennec).- A Nonparametric Classification Algorithm Based on Optimized Templates (J. Kalina).- Light- and Heavy-tailed Density Estimation by Gamma-Weibull Kernel (L. Markovich).- Adaptive Estimation of Heavy Tail Distributions with Application to Hall Model (D. N. Politis, V. A. Vasiliev, S. E. Vorobeychikov).- Extremal Index for a Class of Heavy-tailed Stochastic Processes in Risk Theory (C. Tillier).- Elemental Estimates, Influence, and Algorithmic Leveraging (K. Knight).- Bootstrapping Nonparametric M-Smoothers with Independent Error Terms (M. Maciak).- Probability Bounds for Active Learning in the Regression Problem (A. K. Fermin and C. Ludeña).- Subsampling for Big Data: Some Recent Advances (P. Bertail, O. Jelassi, J. Tressou and M. Zetlaoui).- Extension Sampling Designs for Big Networks: Application to Twitter (A. Rebecq).- Strong Separability in Circulant SSA (J. Bógalo, P. Poncela and E. Senra).- Selection of Window Length in Singular Spectrum Analysis of a Time Series (P. Unnikrishnan and V. Jothiprakash).- Fourier-type Monitoring Procedures for Strict Stationarity (S. Lee, S. G. Meintanis and C. Pretorius).- Wavelet Whittle Estimation in Multivariate Time Series Models: Application to fMRI Data (S. Achard and I. Gannaz).- On Kernel Smoothing with Gaussian Subordinated Spatial Data (S. Ghosh).- Nonparametric and Parametric Methods for Change-Point Detection in Parametric Models (G. Ciuperca).- Variance Estimation Free Tests for Structural Changes in Regression (B. Pestová and M. Pesta).- Index.
| Erscheinungsdatum | 28.01.2019 |
|---|---|
| Reihe/Serie | Springer Proceedings in Mathematics & Statistics |
| Zusatzinfo | IX, 390 p. 53 illus., 26 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 760 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Wirtschaft | |
| Schlagworte | Big Data • dependent data • Heavy-tailed distribution • high-dimensional data • Kernel Methods • machine learning • nonparametric inference • Nonparametric smoother • Nonparametric Statistics • resampling • Statistical Learning • Survey Sampling • Time Series |
| ISBN-10 | 3-319-96940-4 / 3319969404 |
| ISBN-13 | 978-3-319-96940-4 / 9783319969404 |
| Zustand | Neuware |
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