Dependence Models via Hierarchical Structures
Seiten
2025
Cambridge University Press (Verlag)
978-1-009-58411-1 (ISBN)
Cambridge University Press (Verlag)
978-1-009-58411-1 (ISBN)
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.
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.
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.
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).
1. Introduction; 2. Conjugate models; 3. Exchangeable sequences; 4. Markov sequences; 5. General dependent sequences; 6. Temporal dependent sequences; 7. Spatial dependent sequences; 8. Multivariate dependent sequences; Appendix. Data sets; References; Index.
| Erscheinungsdatum | 19.03.2025 |
|---|---|
| Reihe/Serie | Institute of Mathematical Statistics Monographs |
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Gewicht | 362 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| ISBN-10 | 1-009-58411-1 / 1009584111 |
| ISBN-13 | 978-1-009-58411-1 / 9781009584111 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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