Time Series: Modeling, Computation, And Inference (chapman & Hall/crc Texts In Statistical Science)
by Mike West /
2010 / English / PDF
29.7 MB Download
Focusing on Bayesian approaches and computations using
simulation-based methods for inference,
Focusing on Bayesian approaches and computations using
simulation-based methods for inference,Time Series:
Modeling, Computation, and 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 time series modeling and analysis, a broad range of
references to state-of-the-art approaches to univariate and
multivariate time series analysis, and emerging topics at
research frontiers.
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 and analysis, a broad range of
references to state-of-the-art approaches to univariate and
multivariate time series analysis, and emerging topics at
research frontiers.
The book presents overviews of several classes of models and
related methodology for inference, statistical computation for
model fitting and assessment, and forecasting. The authors also
explore the connections between time- and frequency-domain
approaches and develop various models and analyses using Bayesian
tools, such as Markov chain Monte Carlo (MCMC) and sequential
Monte Carlo (SMC) methods. They illustrate the models and methods
with examples and case studies from a variety of fields,
including signal processing, biomedicine, and finance. Data sets,
R and MATLAB
The book presents overviews of several classes of models and
related methodology for inference, statistical computation for
model fitting and assessment, and forecasting. The authors also
explore the connections between time- and frequency-domain
approaches and develop various models and analyses using Bayesian
tools, such as Markov chain Monte Carlo (MCMC) and sequential
Monte Carlo (SMC) methods. They illustrate the models and methods
with examples and case studies from a variety of fields,
including signal processing, biomedicine, and finance. Data sets,
R and MATLAB®
® code, and other material are available
on the authors’ websites.
code, and other material are available
on the authors’ websites.
Along with core models and methods, this text offers
sophisticated tools for analyzing challenging time series
problems. It also demonstrates the growth of time series analysis
into new application areas.
Along with core models and methods, this text offers
sophisticated tools for analyzing challenging time series
problems. It also demonstrates the growth of time series analysis
into new application areas.
