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Dependence Models via Hierarchical Structures: 10 (Institute of Mathematical Statistics Monographs, Series Number 10) - Hardcover
Synopsis
Learn to construct your own dependence models with this step-by-step look at models in a Bayesian analysis context.
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About the Author
Luis E. Nieto-Barajas is Full Professor and Head of the Department of Statistics at the Instituto Tecnológico Autónomo de México (ITAM). He was previously President of the Mexican Statistical Association (2020–2021). For his thesis, he won the Savage Award (2001) and Francisco Aranda Ordaz Awards (2002–2004).
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Dependence Models via Hierarchical Structures (Institute of Mathematical Statistics Monographs, Series Number 10)
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Cambridge University Press, 2025
ISBN 10: 1009584111
ISBN 13: 9781009584111
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Dependence Models via Hierarchical Structures (Institute of Mathematical Statistics Monographs, Series Number 10)
Published by
Cambridge University Press, 2025
ISBN 10: 1009584111
ISBN 13: 9781009584111
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Hardcover
Seller: Books From California, Simi Valley, CA, U.S.A.
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Dependence Models Via Hierarchical Structures
Published by
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ISBN 10: 1009584111
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Dependence Models via Hierarchical Structures (Institute of Mathematical Statistics Monographs, Series Number 10)
Published by
Cambridge University Press, 2025
ISBN 10: 1009584111
ISBN 13: 9781009584111
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Dependence Models Via Hierarchical Structures
Published by
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ISBN 10: 1009584111
ISBN 13: 9781009584111
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Dependence Models via Hierarchical Structures
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Hardback. Condition: New. Bringing together years of research into one useful resource, this text empowers the reader to creatively construct their own dependence models. Intended for senior undergraduate and postgraduate students, it takes a step-by-step look at the construction of specific dependence models, including exchangeable, Markov, moving average and, in general, spatio-temporal models. All constructions maintain a desired property of pre-specifying the marginal distribution and keeping it invariant. They do not separate the dependence from the marginals and the mechanisms followed to induce dependence are so general that they can be applied to a very large class of parametric distributions. All the constructions are based on appropriate definitions of three building blocks: prior distribution, likelihood function and posterior distribution, in a Bayesian analysis context. All results are illustrated with examples and graphical representations. Applications with data and code are interspersed throughout the book, covering fields including insurance and epidemiology. Seller Inventory # LU-9781009584111
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Dependence Models via Hierarchical Structures (Institute of Mathematical Statistics Monographs, Series Number 10)
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Dependence Models via Hierarchical Structures: 10 (Institute of Mathematical Statistics Monographs, Series Number 10)
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ISBN 13: 9781009584111
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Dependence Models Via Hierarchical Structures
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Dependence Models Via Hierarchical Structures
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