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Produktbild: Nonparametric Statistics
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Nonparametric Statistics 4th ISNPS, Salerno, Italy, June 2018

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.11.2020

Abbildungen

X, 547 p. 99 illus., 56 illus. in color.

Herausgeber

Michele La Rocca + weitere

Verlag

Springer

Seitenzahl

547

Maße (L/B/H)

24,1/16/3,4 cm

Gewicht

1100 g

Auflage

20001 Auflage 1st edition 2020

Sprache

Englisch

ISBN

978-3-030-57305-8

Beschreibung

Portrait


Michele La Rocca
is a Full Professor of Statistics at the University of Salerno, Italy. He has published scientific papers on empirical likelihood, nonlinear time series and neural networks, resampling techniques, with applications to biological and financial data. He is an elected member of ISI and a former member of the Charting Committee of the International Society for Nonparametric Statistics.

Brunero Liseo
has been a Full Professor of Statistics at the School of Economics, Sapienza University of Rome, Italy, since 2002. He has published more than 60 papers in international, peer-reviewed journals. His research interests include Bayesian inference, distribution theory, official statistics, and stochastic processes. He is the Director of the Ph.D. School of Economics, Sapienza, and a member of the Advisory Board of ISTAT, and of NADO Italia – the National Anti-Doping Organization, the Italian chapter of WADA.

Luigi Salmaso
is a Full Professor of Statistics at the Department of Management and Engineering at the University of Padua, Italy. He is the author and creator of the NonParametric Combination Test software for multivariate and multistrata permutation tests. He has published more than 100 papers in international, peer-reviewed journals. His main research interests include nonparametric statistics, multivariate analysis, conjoint analysis, big data analytics, and biostatistics. He has received over 3000 citations and written a leading book on permutation testing. He is an Associate Editor for various international ISI journals. 

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.11.2020

Abbildungen

X, 547 p. 99 illus., 56 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

547

Maße (L/B/H)

24,1/16/3,4 cm

Gewicht

1100 g

Auflage

20001 Auflage 1st edition 2020

Sprache

Englisch

ISBN

978-3-030-57305-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Nonparametric Statistics

  • Preface.- Portfolio optimisation via graphical least squares estimation (Saeed Aldahmani, Hongsheng Dai, Qiao-Zhen Zhang and Marialuisa Restaino).- Change of Measure Applications in Nonparametric Statistics (Mayer Alvo).- Choosing between weekly and monthly volatility drivers within a Double Asymmetric GARCH-MIDAS model (Alessandra Amendola, Vincenzo Candila and Giampiero M. Gallo).- Goodness-of-fit test for the baseline hazard rate (Anfriani, A. and Butucea, C. and Gerardin E. and Jeantheau, T. and Lecleire U.).- Permutation tests for multivariate stratified data: synchronized or unsynchronized permutations? (Rosa Arboretti, Eleonora Carrozzo and Luigi Salmaso).- An extension of the dgLARS method to high-dimensional relative risk regression models (Luigi Augugliaro, Ernst C. Wit and Angelo M. Mineo).- A kernel goodness-of-fit test for maximum likelihood density estimates of normal mixtures. (Dimitrios Bagkavos and Prakash N. Patil)- Robust estimation of sparse signal with unknown sparsity cluster value (Eduard Belitser, Nurzhan Nurushev, and Paulo Serra).- Test for sign effect in intertemporal choice experiments: a nonparametric solution (Stefano Bonnini and Isabel Maria Parra Oller).- Nonparametric first-order analysis of spatial and spatio-temporal point processes (M.I. Borrajo, I. Fuentes-Santos and W. González-Manteiga).- Bayesian nonparametric prediction with multi-sample data (Federico Camerlenghi, Antonio Lijoi and Igor Prünster).- Algorithm for Automatic Description of Historical Series of Forecast Error in Electrical Power Grid (Gaia Ceresa, Andrea Pitto, Diego Cirio and Nicolas Omont).- Linear wavelet estimation in regression with additive and multiplicative noise (Christophe Chesneau, Junke Kou and Fabien Navarro).- Speeding up algebraic-based sampling via permutations (Francesca Romana Crucinio and Roberto Fontana).- Obstacle Problems For Nonlocal Operators: A Brief Overview (Donatella Danielli, Arshak Petrosyan, and Camelia A. Pop).- Low and high resonance components restoration in multichannel data (Daniela De Canditiis and Italia De Feis).- Kernel circular deconvolution density estimation (Marco Di Marzio, Stefania Fensore, Agnese Panzera, Charles C. Taylor).- Asymptotic for Relative Frequency when Population is Driven by Arbitrary Unknown Evolution (Silvano Fiorin).- Semantic keywords clustering to optimize Text Ads campaigns (Pietro Fodra, Emmanuel Pasquet, Guillaume Mohr, Bruno Goutorbe, and Matthieu Cornec).- A Note on Robust Estimation of the Extremal Index (M. Ivette Gomes, Cristina Miranda and Manuela Souto de Miranda).- Multivariate permutation tests for ordered categorical data (Huiting Huang, Fortunato Pesarin, Rosa Arboretti, Riccardo Ceccato).- Smooth nonparametric survival analysis (Dimitrios Ioannides and Dimitrios Bagkavos).- Density estimation using multiscale local polynomial transforms (Maarten Jansen).- On Sensitivity of Metalearning: An Illustrative Study for Robust Regression (Jan Kalina).- Function-parametric empirical processes, projections and unitary operators (Estáte Khmaladze).- Rank-based Analysis of Multivariate Data in Factorial Designs and Its Implementation in R (Maximilian Kiefel and Arne C. Bathke).- Tests for Independence Involving Spherical Data (Pierre Lafaye de Micheaux, Simos Meintanis and Thomas Verdebout).- Interval-Wise Testing of Functional Data Defined on Two-dimensional Domains(Patrick B. Langthaler, Alessia Pini and Arne C. Bathke).- Assessing Data Support for the Simplifying Assumption in Bivariate Conditional Copulas (Evgeny Levi and Radu V. Craiu).- Semiparametric weighting estimations of a zero-inflated Poisson regression with missing in covariates (Lukusa, M.T. and Phoa, F.K.H.).- The Discrepancy Method for Extremal Index Estimation (Natalia Markovich).- Correction for optimisation bias in structured sparse high-dimensional variable selection (Bastien Marquis and Maarten Jansen).- United Statistical Algorithms and Data Science: An Introduction To The Principles (Subhadeep Mukhopadhyay).- The Halfspace Depth Characterization Problem (Stanislav Nagy).- A component multiplicative error model for realized volatility measures (Antonio Naimoli and Giuseppe Storti).- Asymptotically distribution-free goodness-of-fit tests for testing independence in contingency tables of large dimensions (Thuong T. M. Nguyen).- Incorporating model uncertainty in the construction of bootstrap prediction intervals for functional time series (Efstathios Paparoditis and Han Lin Shang).- Measuring and Estimating Overlap of Distributions: A comparison of approaches from various disciplines (Judith H. Parkinson and Arne C. Bathke).- Bootstrap confidence intervals for sequences of missing values in multivariate time series (Maria Lucia Parrella, Giuseppina Albano, Michele La Rocca and Cira Perna).- On Parametric Estimation of Distribution Tails (Igor Rodionov).- An empirical comparison of global and local functional depths (Carlo Sguera and Rosa E. Lillo).- AutoSpec: Detecting Exiguous Frequency Changes in Time Series (David S. Stoffer).- Bayesian quantile regression in differential equation models (Qianwen Tan and Subhashis Ghosal).- Predicting plant endemicity based on herbarium data: application to French data (Jessica Tressou, Thomas Haevermans and Liliane Bel).- Monte Carlo Permutation Tests for Assessing Spatial Dependence at Different Scales (Craig Wang and Reinhard Furrer).- Introduction to independent counterfactuals (Marcin Wolski).- The Potential for Nonparametric Joint Latent Class Modeling of Longitudinal and Time-to-Event Data (Ningshan Zhang and Jeffrey S. Simonoff).- To Rank or to Permute when Comparing an Ordinal Outcome Between Two Groups While Adjusting for a Covariate? (Georg Zimmermann).