Del 74 - Springer Proceedings in Mathematics & Statistics

Topics in Nonparametric Statistics

Proceedings of the First Conference of the International Society for Nonparametric Statistics

Inbunden, Engelska, 2014

Av Michael G. Akritas, S. N. Lahiri, Dimitris N. Politis

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This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for Non Parametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI and other organizations. M.G. Akritas, S.N. Lahiri and D.N. Politis are the first executive committee members of ISNPS and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world.The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the world and contributes to the further development of the field. The conference program included over 250 talks, including special invited talks, plenary talks and contributed talks on all areas of nonparametric statistics. Out of these talks, some of the most pertinent ones have been refereed and developed into chapters that share both research and developments in the field.

Produktinformation

Utgivningsdatum2014-12-03
Mått155 x 235 x 27 mm
Vikt740 g
FormatInbunden
SpråkEngelska
SerieSpringer Proceedings in Mathematics & Statistics
Antal sidor367
Upplaga2014
FörlagSpringer-Verlag New York Inc.
ISBN9781493905683

Tillhör följande kategorier

A cost based reweighed scheme of Principal Support Vector Machine.- Power assessment of a new test of independence.- Empirical.- Divergence Minimizers for Hadamard Differentiable Functionals.- Advances in permutation tests for covariates in a mixture model for preference data analysis.- Semiparametric generalized estimating equations in misspecified models.- Local Covariance Estimation using Costationarity.- Stable Non-parametric Signal Filtration in Nonlinear Models.- Block bootstrap for the autocovariance coefficients of periodically correlated time series.- Bootstrapping Realized Bipower Variation.- Multiple testing approaches for removing background noise from images.- Peak-Load Forecasting using a Functional Semi-Parametric Approach.- An open problem on strongly consistent learningof the best prediction for Gaussian processes.- Testing for equality of an increasing number of spectral density functions.- Multiscale local polynomial models for estimation and testing.- Distributions of Clusters of Exceedances and Their Applications in Telecommunication Networks.- Diagnostic procedures for spherically symmetric distributions.- A nonparametric causality test: Detection of direct causal effects in multivariate systems using Corrected Partial Transfer Entropy.- A high performance biomarker detection method for exhaled breath mass spectrometry data.- Local polynomials for variable selection.- On the CLT on Low Dimensional Stratified Spaces- Kernel Density Outlier Detector.- Model Selection for Classification with a Large Number of Classes.-Semiparametric Bayesian Small Area Estimation Based on Dirichlet Process Priors.- Bootstrap confidence intervals in nonparametric regression without an additive model.- Heteroskedastic linear regression: steps towards adaptivity, efficiency, and robustness- Level Set Estimation.- Nonparametric Permutation-Based Control Charts for Ordinal Data.- The latest advances on the Hill estimator and its modifications.- Multivariate Stochastic Volatility Estimation Using Particle Filters.- Guaranteed Estimation of Logarithmic Density Derivative by Dependent Observations.- Nonparametric Statistics and High/Infinite Dimensional Data.