| 000 | 02819nam a2200289 a 4500 | ||
|---|---|---|---|
| 001 | vtls000051597 | ||
| 003 | KUKTEM | ||
| 005 | 20251117150457.0 | ||
| 008 | 110303t2010 flua f b 001 0 eng d | ||
| 020 | _a9781420093360 (hardback : acid-free paper) | ||
| 039 | 9 |
_a201107140058 _bVLOAD _c201106301118 _dfauzi _y201103031019 _zsri |
|
| 040 | _aUMP | ||
| 090 | _aQA280 .P73 2010 | ||
| 100 | 1 | _aPrado, Raquel | |
| 245 | 1 | 0 |
_aTime series : _bmodeling, computation, and inference / _cRaquel Prado, Mike West |
| 260 |
_aBoca Raton : _bCRC Press, _cc2010 |
||
| 300 |
_axx, 353 p. : _bill. ; _c25 cm. |
||
| 490 | 1 | _aChapman & Hall/CRC texts in statistical science series | |
| 500 | _a"A Chapman & Hall Book" | ||
| 504 | _aIncludes bibliographical references (p. 321-337) and indexes | ||
| 505 | 0 | _aNotation, definitions, and basic inference -- Traditional time domain models -- The frequency domain -- Dynamic linear models -- State-space TVAR models -- SMC 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 | |
| 520 | _a"Focusing on Bayesian approaches and computations using up-to-date 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 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® 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."--Publisher’s description | ||
| 650 | 0 |
_aTime-series analysis _vTextbooks |
|
| 700 | 1 | _aWest, Mike | |
| 830 | 0 | _aTexts in statistical science | |
| 999 |
_aVIRTUA40 _c59137 _d59143 |
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