2nd Edition

Time Series Modeling, Computation, and Inference, Second Edition

472 Pages 116 B/W Illustrations
by Chapman & Hall

472 Pages 116 B/W Illustrations
by Chapman & Hall

472 Pages 116 B/W Illustrations
by Chapman & Hall

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... Read more
1. Notation, definitions, and basic inference
2. Traditional time domain models
3. The frequency domain
4. Dynamic linear models
5. State-space TVAR models
6. SMC methods for state-space models
7. Mixture models in time series
8. Topics and examples in multiple time series
9. Vector AR and ARMA models
10. General classes of multivariate dynamic models
11. Latent factor models

Biography

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.