New Trends in Functional Statistics and Related Fields
Springer International Publishing (Verlag)
978-3-031-92382-1 (ISBN)
This volume gathers peer-reviewed contributions presented at the 6th International Workshop on Functional and Operatorial Statistics, IWFOS 2025, held in Novara, Italy, June 25-27, 2025.
Covering a broad spectrum of topics in functional and operatorial statistics and related fields, including high-dimensional statistics and machine learning, the contributions tackle both fundamental theoretical challenges and practical applications. A variety of features of statistics for functional data are addressed, such as estimation of functional features, exploration and pre-processing of functional data, methodologies for functional regression and forecasting problems, unsupervised and supervised classification, and testing procedures. Nonstandard functional data and situations which go beyond the pattern of samples of independent variables are investigated, and a link to the field of artificial intelligence is presented. Interesting real data applications to medicine, health, economics and the natural, environmental and social sciences are featured throughout.
Initiated at the University of Toulouse in 2008, the series of IWFOS workshops fosters discussion and international collaboration on theoretical advancements, methodological innovations, and applications in functional and operatorial statistics and related fields.
Chapter 42 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Germán Aneiros is a Full Professor of Statistics at the University of A Coruña, Spain. His main research interest focuses on scalar-on-function regression models, covering linear, nonparametric, partial linear and single index regression. His interests also include the case of high-dimensional covariates. He is an Associate Editor of the journals Computational Statistics and Journal of Multivariate Analysis.
Enea G. Bongiorno is an Associate Professor in Statistics at Università del Piemonte Orientale in Novara, Italy. His interests include non- and semi-parametric methods and small ball probability for functional data. He is a fellow of the Bernoulli Society, and IASC (International Association for Statistical Computing) of which he was scientific secretary of the European Regional Section and on the board of directors. He is an Associate Editor of the journals Computational Statistics & Data Analysis and Computational Statistics.
Aldo Goia is a Full Professor of Statistics at Università del Piemonte Orientale in Novara, Italy. His research focuses on statistical methods for functional data and in particular on non-parametric and semi-parametric regression models, the small ball probability factorization and the study of complexity. He is an Associate Editor of the journal Computational Statistics.
Marie Hu ková is a Full Professor of Mathematical Statistics at Charles University in Prague, Czech Republic. She is the author of more than 130 scientific papers, mainly on asymptotic statistics, nonparametric and multivariate statistics and change-point problems. She is an Associate Editor of the journals Metrika, Statistics, and Sequential Analysis, and is a former Associate Editor of the Journal of Statistical Planning and Inference and REVSTAT. She is an elected member of ISI and a fellow of IMS. For several years, she was the Chair of the European Regional Committee of the Bernoulli Society and a member of the Council of ISI.
1 Germán Aneiros, Enea G. Bongiorno, Aldo Goia and Marie Hu ková, An Introduction to the 6th Edition of the International Workshop on Functional and Operatorial Statistics.- 2 Nihan Acar-Denizli and Pedro Delicado, Local Constant Likelihood Estimation for Beta Distribution with Time Varying Parameters.- 3 Mohamed Alahiane, Mustapha Rachdi, Idir Ouassou and Philippe Vieu, An Expansion of the Functional Projection Pursuit Regression to Generalized Partially Linear Single Index Models.- 4 Alexander Aue, Sebastian Kühnert and Gregory Rice, On the Estimation of Invertible Functional Time Series.- 5 Patrick Bastian, Rupsa Basu and Holger Dette, Uniform Confidence Bands for Joint Angles Across Different Fatigue Phases.- 6 Sayan Bhadra and Anuj Srivastava, Scalar on Shape Regression Using Function Data.- 7 Filip Bocinec, Erik Mendro and Stanislav Nagy, A Comparison of Band-based Approaches to Functional Depth.- 8 Enea G. Bongiorno, Lax Chan and Aldo Goia, Analysing the Complexity Mixture Structure of Daily Probability Densities of Bitcoin Returns.- 9 Teresa Bortolotti, Roberta Troilo, Alessandra Menafoglio and Simone Vantini, Regularized Nonparametric Estimation of Covariance Kernels for High-Dimensional Interferometric Data.- 10 Alain Boudou and Sylvie Viguier-Pla, Statistical Properties of a Random Series Transmitted by Filtering.- 11 Robert Cantwell and John Aston for the Alzheimer s Disease Neuroimaging Initiative, Multi-Object Regression: A Linear Framework via Partial Least Squares.- 12 Christian Capezza, Davide Forcina, Antonio Lepore, Biagio Palumbo, Monitoring the Covariance of Multichannel Profiles.- 13 Hervé Cardot and Caroline Peltier, Statistical Modeling of Categorical Trajectories with Multivariate Functional Data Approaches.- 14 Roberto Casarin, Radu Craiu and Qing Wang, Markov Switching Tensor Regressions.- 15 Michele Cavazzutti, Eleonora Arnone, Ying Sun, Marc G. Genton and Laura M. Sangalli, Functional Data Depth for the Analysis of Earth Surface Temperatures.- 16 Lax Chan, Laurent Delsol and Aldo Goia, Improving Finite Samples Performances in Nonparametric Functional Regression by Using Weighted Pseudo-Metrics.- 17 Aldo Clemente, Alessandro Palummo, Eleonora Arnone and Laura M. Sangalli, Smoothing with Nonlinear Partial Differential Equation Regularization.- 18 Adéla Czolková, Karel Hron and Sonja Greven, Functional Principal Component Analysis for Bivariate Densities and their Orthogonal Decomposition.- 19 Marco F. De Sanctis, Ilenia Di Battista, Eleonora Arnone, Cristian Castiglione, Mauro Bernardi, Francesca Ieva and Laura M. Sangalli, Estimating Multiple Quantile Surfaces: A Penalized Functional Approach.- 20 Simone Di Gregorio and Francesco Iafrate, Neural Drift Estimation for Ergodic Diffusions: Nonparametric Analysis and Numerical Exploration.- 21 Jacopo Di Iorio, Marzia A. Cremona and Francesca Chiaromonte, Amplitude-Invariant Functional Motif Discovery.- 22 Daniel Diz-Castro, Manuel Febrero-Bande and Wenceslao González-Manteiga, Testing the Significance of Covariates in Nonparametric Regression without the Curse of Dimensionality.- 23 Patric Dolmeta and Matteo Giordano, Gaussian Process Methods for Covariate-Based Intensity Estimation.- 24 Mélanie Dreina, Sylvie Viguier-Pla and Stéphane Abide, Spectral Analysis of Multidimensional Thermal Fields.- 25 Matteo Farnè and Xuanye Dai, Forecasting Dynamic Factor Scores by UNALSE Spectral Density Matrix Estimator.- 26 Manuel Febrero-Bande, Pedro Galeano and Wenceslao González-Manteiga, Testing for Linearity and Independence in Scalar-on-Function Regression with Responses Missing at Random by Generalized Distance Covariance.-
| Erscheinungsdatum | 25.05.2025 |
|---|---|
| Reihe/Serie | Contributions to Statistics |
| Zusatzinfo | XXXVI, 567 p. 127 illus., 88 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | Bayesian inference • classification • compositional data • Depth Measures • dimensionality reduction • functional data analysis • Functional time series • machine learning • Nonparametric Statistics • regression models • Testing |
| ISBN-10 | 3-031-92382-0 / 3031923820 |
| ISBN-13 | 978-3-031-92382-1 / 9783031923821 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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