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Other titles in the Wiley Series in Probability & Mathematical Statistics series:
A Course in Time Series Analysis (Wiley Series in Probability & Mathematical Statistics)by Daniel Pena
Synopses & Reviews
New 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:
Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.
Book News Annotation:
The time series, a sequence of observations taken at regular intervals, is frequently used to organize data in business, economics, engineering, the environment, medicine, and other areas; examples include daily stock prices, weekly traffic volume, and annual growth rates. This text demonstrates how to build time series models for univariate and multivariate time series data. It covers basic concepts , such as ARIMA models, the Kalman filter, and signal extraction, as well as more advanced topics including heteroscedastic models, nonlinear time series models, and Bayesian time series analysis.
Annotation c. Book News, Inc., Portland, OR (booknews.com)
About the Author
DANIEL 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.
Table of Contents
Introduction (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).
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