Statistical Dependence Modeling: Festschrift in Honor of Claudia Czado
Language: English
Published by Springer, 2026
- Hardcover
- New

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- Title
- Statistical Dependence Modeling: Festschrift in Honor of Claudia Czado
- Publisher
- Springer
- Publication year
- 2026
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3032142512
- ISBN 13
- 9783032142511
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.
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About the Author
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.
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