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Full Description
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.
Contents
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.
