Time Series: Modeling, Computation, and Inference, 1st Edition (e-Book) book cover

Time Series

Modeling, Computation, and Inference, 1st Edition

By Raquel Prado, Mike West

Chapman and Hall/CRC

368 pages

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pub: 2010-05-21
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Description

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference 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 t

Table of Contents

Notation, Definitions, and Basic Inference. Traditional Time Domain Models. The Frequency Domain. Dynamic Linear Models. State-Space Time-Varying Autoregressive Models. Sequential Monte Carlo Methods for State-Space Models. Mixture Models in Time Series. Topics and Examples in Multiple Time Series. Vector AR and ARMA Models. Multivariate DLMs and Covariance Models. Indices. Bibliography.

About the Authors

Raquel Prado is an associate professor in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz.

Mike West is the Arts & Sciences Professor of Statistical Science in the Department of Statistical Science at Duke University.

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General