Dependence Models via Hierarchical Structures: 10 (Institute of Mathematical Statistics Monographs, Series Number 10) - Hardcover

Luis E. Nieto-Barajas

9781009584111: Dependence Models via Hierarchical Structures: 10 (Institute of Mathematical Statistics Monographs, Series Number 10)

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ISBN 10: 1009584111 ISBN 13: 9781009584111

Publisher: Cambridge University Press, 2025

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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 Tecnol�gico Aut�nomo de M�xico (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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Publisher: Cambridge University Press
Publication date: 2025
Language: English
ISBN 10: 1009584111
ISBN 13: 9781009584111
Binding: Hardcover
Number of pages: 157

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Dependence Models via Hierarchical Structures (Institute of Mathematical Statistics Monographs, Series Number 10)

Nieto-Barajas, Luis E.

Published by 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)

Nieto-Barajas, Luis E.

Published by Cambridge University Press, 2025

ISBN 10: 1009584111 ISBN 13: 9781009584111

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Dependence Models Via Hierarchical Structures

Nieto-barajas, Luis E.

Published by Cambridge University Press, 2025

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Dependence Models via Hierarchical Structures (Hardcover)

Luis E. Nieto-Barajas

Published by Cambridge University Press, Cambridge, 2025

ISBN 10: 1009584111 ISBN 13: 9781009584111

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Hardcover. Condition: new. Hardcover. 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. Intended for senior undergraduate and postgraduate students, this text explores how to construct dependence models including exchangeable, Markov, temporal and spatial models. Readers are empowered to be creative and construct their own dependence models. Examples appear throughout, and multiple applications with data and code are provided. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009584111

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