Dependence Modeling
Dependence Modeling

Author: Harry Joe

Publisher: World Scientific

Published: 2011

Total Pages: 370

ISBN-13: 981429988X

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1. Introduction : Dependence modeling / D. Kurowicka -- 2. Multivariate copulae / M. Fischer -- 3. Vines arise / R.M. Cooke, H. Joe and K. Aas -- 4. Sampling count variables with specified Pearson correlation : A comparison between a naive and a C-vine sampling approach / V. Erhardt and C. Czado -- 5. Micro correlations and tail dependence / R.M. Cooke, C. Kousky and H. Joe -- 6. The Copula information criterion and Its implications for the maximum pseudo-likelihood estimator / S. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. Counting vines / O. Morales-Napoles -- 10. Regular vines : Generation algorithm and number of equivalence classes / H. Joe, R.M. Cooke and D. Kurowicka -- 11. Optimal truncation of vines / D. Kurowicka -- 12. Bayesian inference for D-vines : Estimation and model selection / C. Czado and A. Min -- 13. Analysis of Australian electricity loads using joint Bayesian inference of D-vines with autoregressive margins / C. Czado, F. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka

Dependence Modeling with Copulas
Dependence Modeling with Copulas

Author: Harry Joe

Publisher: CRC Press

Published: 2014-06-26

Total Pages: 479

ISBN-13: 1466583231

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Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured facto

Dependence Modeling with Copulas
Dependence Modeling with Copulas

Author: Harry Joe

Publisher: CRC Press

Published: 2014-06-26

Total Pages: 483

ISBN-13: 1466583223

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Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.

Uncertainty Analysis with High Dimensional Dependence Modelling
Uncertainty Analysis with High Dimensional Dependence Modelling

Author: Dorota Kurowicka

Publisher: John Wiley & Sons

Published: 2006-10-02

Total Pages: 302

ISBN-13: 0470863080

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Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

Copulas and Dependence Models with Applications
Copulas and Dependence Models with Applications

Author: Manuel Úbeda Flores

Publisher: Springer

Published: 2017-10-13

Total Pages: 268

ISBN-13: 3319642219

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This book presents contributions and review articles on the theory of copulas and their applications. The authoritative and refereed contributions review the latest findings in the area with emphasis on “classical” topics like distributions with fixed marginals, measures of association, construction of copulas with given additional information, etc. The book celebrates the 75th birthday of Professor Roger B. Nelsen and his outstanding contribution to the development of copula theory. Most of the book’s contributions were presented at the conference “Copulas and Their Applications” held in his honor in Almería, Spain, July 3-5, 2017. The chapter 'When Gumbel met Galambos' is published open access under a CC BY 4.0 license.

Multivariate Models and Multivariate Dependence Concepts
Multivariate Models and Multivariate Dependence Concepts

Author: Harry Joe

Publisher: CRC Press

Published: 1997-05-01

Total Pages: 422

ISBN-13: 9780412073311

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This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems.

Modeling Dependence in Econometrics
Modeling Dependence in Econometrics

Author: Van-Nam Huynh

Publisher: Springer Science & Business Media

Published: 2013-11-18

Total Pages: 570

ISBN-13: 3319033956

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In economics, many quantities are related to each other. Such economic relations are often much more complex than relations in science and engineering, where some quantities are independence and the relation between others can be well approximated by linear functions. As a result of this complexity, when we apply traditional statistical techniques - developed for science and engineering - to process economic data, the inadequate treatment of dependence leads to misleading models and erroneous predictions. Some economists even blamed such inadequate treatment of dependence for the 2008 financial crisis. To make economic models more adequate, we need more accurate techniques for describing dependence. Such techniques are currently being developed. This book contains description of state-of-the-art techniques for modeling dependence and economic applications of these techniques. Most of these research developments are centered around the notion of a copula - a general way of describing dependence in probability theory and statistics. To be even more adequate, many papers go beyond traditional copula techniques and take into account, e.g., the dynamical (changing) character of the dependence in economics.

Interpretable Machine Learning
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Renewable Energy Integration to the Grid
Renewable Energy Integration to the Grid

Author: Neeraj Gupta

Publisher: CRC Press

Published: 2022-03-24

Total Pages: 275

ISBN-13: 100055144X

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This comprehensive reference text discusses uncertainty modeling of renewable energy resources and its steady state analysis. The text discusses challenges related to renewable energy integration to the grid, techniques to mitigate these challenges, problems associated with integration at transmission and distribution voltage level, and protection of power system with large renewable power integration. It covers important concepts including voltage issues in power networks, use of FACTS devices for reactive power management, stochastic optimization, robust optimization, and spatiotemporal dependence modeling. Key Features: Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers. Discusses dependence modeling of multi-site renewable generations in detail. Covers probabilistic analysis, useful for data analysts. Discusses various aspects of renewable energy integration i.e. technical, economic, etc. Covers correlation factors, and methodologies are validated with case studies with various standard test systems. The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.

Analyzing Dependent Data with Vine Copulas
Analyzing Dependent Data with Vine Copulas

Author: Claudia Czado

Publisher:

Published: 2019

Total Pages:

ISBN-13: 9783030137861

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This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications. It focuses on statistical estimation and selection methods for vine copulas in data applications. These flexible copula models can successfully accommodate any form of tail dependence and are vital to many applications in finance, insurance, hydrology, marketing, engineering, chemistry, aviation, climatology and health. The book explains the pair-copula construction principles underlying these statistical models and discusses how to perform model selection and inference. It also derives simulation algorithms and presents real-world examples to illustrate the methodological concepts. The book includes numerous exercises that facilitate and deepen readers understanding, and demonstrates how the R package VineCopula can be used to explore and build statistical dependence models from scratch. In closing, the book provides insights into recent developments and open research questions in vine copula based modeling. The book is intended for students as well as statisticians, data analysts and any other quantitatively oriented researchers who are new to the field of vine copulas. Accordingly, it provides the necessary background in multivariate statistics and copula theory for exploratory data tools, so that readers only need a basic grasp of statistics and probability.