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Time Series: Modeling, Computation, and Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) - Tapa dura

Libro 84 de 126: Chapman & Hall/CRC Texts in Statistical Science

Prado, Raquel; Ferreira, Marco A. R.; West, Mike

 
9781498747028: Time Series: Modeling, Computation, and Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
Tapa dura
ISBN 10: 1498747027 ISBN 13: 9781498747028
Editorial: Chapman and Hall/CRC, 2021

Sinopsis

Winner of the 2025 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting.

It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance.

Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges.

New in the second edition:

  • Expanded on aspects of core model theory and methodology.
  • Multiple new examples and exercises.
  • Detailed development of dynamic factor models.
  • Updated discussion and connections with recent and current research frontiers.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Raquel Prado is Professor in the Department of Statistics at the Baskin School of Engineering of the University of California Santa Cruz, USA. Her main research areas are time series analysis and Bayesian modeling - with a focus on analysis of large-dimensional nonstationary time series data and applications to biomedical signal processing and brain imaging. Marco A. R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Tech, where he served from 2016 to 2020 as the Director of Graduate Programs. Mike West holds a Duke University distinguished chair as the Arts & Sciences Professor of Statistics & Decision Sciences in the Department of Statistical Science, where he led the development of statistics from 1990-2002.

De la contraportada

This is the second edition of a popular graduate level textbook on time series modeling, computation and inference. The book is essentially unique in its approach, with a focus on Bayesian methods, although classical methods are also covered.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Chapman and Hall/CRC
Año de publicación
2021
Idioma
Inglés
ISBN 10 
1498747027
ISBN 13 
9781498747028
Encuadernación
Tapa dura
Número de edición
2
Número de páginas
452
Contacto del fabricante
no disponible
Persona responsable
no disponible