Time Series: Modeling, Computation, and Inference (Chapman & Hall/CRC Texts in Statistical S... Book

Notation, Definitions, and Basic Inference
Problem areas and objectives Stochastic processes and stationarity Autocorrelation and cross-correlation functions Smoothing and differencing A primer on likelihood and Bayesian inference

Traditional Time Domain Models
Structure of autoregressions Forecasting Estimation in autoregressive (AR) models Further issues on Bayesian inference for AR models Autoregressive moving average (ARMA) models Other models

The Frequency Domain
Harmonic regression Some spectral theory Discussion and extensions

Dynamic Linear Models
General linear model structures Forecast functions and model forms Inference in dynamic linear models (DLMs): basic normal theory Extensions: non-Gaussian and nonlinear models Posterior simulation: Markov chain Monte Carlo (MCMC) algorithms

State-Space Time-Varying Autoregressive Models
Time-varying autoregressions (TVAR) and decompositions TVAR model specification and posterior inference Extensions

Sequential Monte Carlo Methods for State-Space Models
General state-space models Posterior simulation: sequential Monte Carlo (SMC)

Mixture Models in Time Series
Markov switching models Multiprocess models Mixtures of general state-space models Case study: detecting fatigue from EEGs Univariate stochastic volatility models

Topics and Examples in Multiple Time Series
Multichannel modeling of EEG data Some spectral theory Dynamic lag/lead models Other approaches

Vector AR and ARMA Models
Vector AR (VAR) models Vector ARMA (VARMA) models Estimation in VARMA Extensions: mixtures of VAR processes

Multivariate DLMs and Covariance Models
Theory of multivariate and matrix normal DLMs Multivariate DLMs and exchangeable time series Learning cross-series covariances Time-varying covariance matrices Multivariate dynamic graphical models

Author Index
Subject Index

Bibliography

Problems appear at the end of each chapter.