Statistical Dependence Modeling
Springer International Publishing (Verlag)
978-3-032-14251-1 (ISBN)
- Noch nicht erschienen - erscheint am 01.06.2026
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
This volume on statistical dependence modeling is published in honor of Claudia Czado and her influential career in statistics. Reflecting the breadth of her research interests, the book presents authoritative peer-reviewed contributions on theoretical foundations, methodological innovations, and applications in dependence modeling, statistical methodology and Bayesian computation. It also features two historical accounts of vine copulas, a field that Claudia Czado has significantly influenced and contributed to. The book serves both as a scholarly resource and as a celebration of her scientific accomplishments.
Thomas Nagler is a Professor of Computational Statistics & Data Science at the Department of Statistics, LMU Munich, Germany, and a principal investigator at the Munich Center for Machine Learning (MCML). His research interests include high-dimensional dependence models, statistical machine learning, time series, and statistical computing.
Dorota Kurowicka is an Associate Professor at the Delft Institute of Applied Mathematics, TU Delft, The Netherlands. Her research interests include risk analysis, uncertainty analysis, multivariate dependence models and graphical models.
Roger Cooke is a Senior Fellow Emeritus at Resources for the Future, Washington, D.C., USA, and also emeritus professor of mathematics at the Delft University of Technology. He is an expert on expert judgment, uncertainty analysis, mathematical modeling of risk and high dimensional dependence modeling.
Harry Joe is a Professor at the Department of Statistics at the University of British Columbia, Vancouver, BC, Canada. His research interests include dependence modeling, non-normal time series, extreme value inference and inference based on low-dimensional margins.
Preface.- Kjersti Aas, Roger Cooke, Harry Joe, Dorota Kurowicka, Thomas Nagler Growth cycle of vine copulas.- Nicole Barthel, Dominik Müller, Eike Brechmann A decade of elevating (with) vine copulas.- Panagiotis Serrano, Dorota Kurowicka Stochastic simulation inference algorithm in restricted Pair Copula Bayesian Network with single root node.- Manfred Denker, Aleksey Min Gibbs copulas.- Harry Joe: Diagnostics for the validity of the simplifying assumption for vine copulas.- Roger M. Cooke, Tim J. Bedford Identifiability and the simplifying assumption.- Alexis Derumigny Measures of non simplifyingness for conditional copulas and vines.- Thomas Nagler, Gerda Claeskens, Irène Gijbels On dimension reduction in conditional dependence models.- Canyi Chen, Ritoban Kundu, Wei Hao, Peter Song Copula structural equation models for mediation pathway analysis.- Ozan Evkaya, Ariane Hanebeck, Özge Sahin Clusterspecific ranking and variable importance for Scottish regional deprivation via vine mixtures.- Ilias Willems, Sara Rutten, Gilles Crommen, Ingrid van Keilegom: A flexible control function approach for survival data subject to different types of censoring.- Annette Möller, David Jobst, Ferdinand Buchner Vine copula based probabilistic weather forecasting - Review, challenges and future work.- Karoline Bax, Alessandro Fulci, Sandra Paterlini, Emanuele Taufer Generalized precision matrices for non Gaussian distributions Theory and portfolio applications.- Matthias Fischer LogTukey Type distributions as models for operational losses.- Gregor Zens, Sylvia Frühwirth Schnatter Marginal data augmentation for efficient Bayesian modeling of counts and rates with a demographic application.
| Erscheint lt. Verlag | 1.6.2026 |
|---|---|
| Zusatzinfo | Approx. 350 p. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Wirtschaft | |
| Schlagworte | Bayesian Computation • Bayesian inference • Copula • Dependence Modeling • multivariate analysis • statistical modeling • Vine Copula |
| ISBN-10 | 3-032-14251-2 / 3032142512 |
| ISBN-13 | 978-3-032-14251-1 / 9783032142511 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
