A Course in Time Series Analysis
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BeschreibungNew statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:
- Contributions from eleven of the world' s leading figures in time series
- Shared balance between theory and application
- Exercise series sets
- Many real data examples
- Consistent style and clear, common notation in all contributions
- 60 helpful graphs and tables
InhaltsverzeichnisIntroduction (D. Pe?a & G. Tiao). BASIC CONCEPTS IN UNIVARIATE TIME SERIES. Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, State Space Model (G. Wilson). Univariate Autoregressive Moving Average Models (G. Tiao). Model Fitting and Checking, and the Kalman Filter (G. Wilson). Prediction and Model Selection (D. Pe?a). Outliers, Influential Observations and Missing Data (D. Pe?a). Automatic Modeling Methods for Univariate Series (V. Gomez & A. Maravall). Seasonal Adjustment and Signal Extraction in Economic Time Series (V. Gomez & A. Maravall). ADVANCED TOPICS IN UNIVARIATE TIME SERIES. Heteroscedatic Models (R. Tsay). Nonlinear Time Series Models (R. Tsay). Bayesian Time Series Analysis (R. Tsay). Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression and Quantile Regression (S. Heiler). Neural Networks (K. Hornik & F. Leisch). MULTIVARIATE TIME SERIES. Vector ARMA Models (G. Tiao). Cointegration in the VAR Model (S. Johansen). Multivariate Linear Systems (M. Deistler). References. Index.
PortraitDANIEL PE?A, PhD, is Professor of Statistics, Universidad Carlos III de Madrid. GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago. RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.
Pressestimmen"This text demonstrate how to build time series models for univariate and multivariate time series data."(SciTech Book News, Vol. 25, No. 2, June 2001) "...material is thoroughly and carefully presented...a very useful addition to any collection both for learning and reference." (Short Book Reviews, Vol. 21, No. 2, August 2001) "From the preface: 'The book can be used as a principal text or a complementary text for courses in time series.'" (Mathematical Reviews, Issue 2001k) "...an excellent complement...for a first graduate course in time series analysis...a nice addition to anyone's time series library." (Technometrics, Vol. 43, No. 4, November 2001) "...an excellent source of introductory surveys of several timely topics in time series analysis..." (Statistical Papers, July 2002) "...a nice compendium covering a lot of relevant material..." (Statistics & Decisions, Vol.20, No.4, 2002)
Untertitel: 'Wiley Series in Probability &'. New. Sprache: Englisch.
Verlag: JOHN WILEY & SONS INC
Erscheinungsdatum: Dezember 2000
Seitenanzahl: 496 Seiten