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190425 ||| eng |
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|a 9783319969411
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| 100 |
1 |
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|a Bertail, Patrice
|e [editor]
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| 245 |
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|a Nonparametric Statistics
|h Elektronische Ressource
|b 3rd ISNPS, Avignon, France, June 2016
|c edited by Patrice Bertail, Delphine Blanke, Pierre-André Cornillon, Eric Matzner-Løber
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| 250 |
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|a 1st ed. 2018
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2018, 2018
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| 300 |
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|a IX, 390 p. 53 illus., 26 illus. in color
|b online resource
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| 505 |
0 |
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|a 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) --
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|a 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. Vaičiulis) -- 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) --
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| 505 |
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|a 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. Peštová and M. Pešta) -- Index
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Statistics
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| 653 |
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|a Statistics and Computing/Statistics Programs
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| 653 |
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|a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Statistics for Life Sciences, Medicine, Health Sciences
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| 653 |
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|a Statistics for Business, Management, Economics, Finance, Insurance
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| 700 |
1 |
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|a Blanke, Delphine
|e [editor]
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| 700 |
1 |
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|a Cornillon, Pierre-André
|e [editor]
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| 700 |
1 |
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|a Matzner-Løber, Eric
|e [editor]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Springer Proceedings in Mathematics & Statistics
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-96941-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 519.5
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| 520 |
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|a 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
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