9780387989501

Time Series Analysis and Its Applications

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  • ISBN13:

    9780387989501

  • ISBN10:

    0387989501

  • Format: Hardcover
  • Copyright: 2000-02-01
  • Publisher: Springer Verlag
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Supplemental Materials

What is included with this book?

Summary

Time Series Analysis and Its Applications presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. The book is designed to be useful as a text for graduate level students in the physical, biological and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course.Theory and methodology are separated to allow presentations on different levels. Material from the earlier 1988 Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware.Robert H. Shumway is Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the Inernational Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is the author of a previous 1988 Prentice-Hall text on applied time series analysis and is currenlty a Departmental Editor for the Journal of Forecasting.David S. Stoffer is Professor of Statistics at the University of Pittsburgh. He has made seminal contributions to the analysis of categorical time series and won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently an Associate Editor of the Journal of Forecasting and has served as an Associate Editor for the Journal fo the American Statistical Association.

Author Biography

David S. Stoffer is Professor of Statistics of the University of Pittsburgh.

Table of Contents

Preface vii
Characteristics of Time Series
Introduction
1(3)
The Nature of Time Series Data
4(5)
Time Series Statistical Models
9(6)
Measures of Dependence: Auto and Cross Correlation
15(4)
Stationary Time Series
19(6)
Estimation of Correlation
25(6)
Exploratory Data Analysis
31(7)
Classical Regression and Smoothing in the Time Series Context
38(13)
Vector Valued and Multidimensional Series
51(5)
Convergence Modes
56(9)
Central Limit Theorems
65(4)
The Mean and Autocorrelation Functions
69(20)
Problems
78(11)
Time Series Regression and ARIMA Models
Introduction
89(1)
Autoregressive Moving Average Models
90(12)
Homogeneous Difference Equations
102(5)
Autocorrelation and Partial Autocorrelation Functions
107(7)
Forecasting
114(11)
Estimation
125(17)
Integrated Models for Nonstationary Data
142(2)
Building ARIMA Models
144(11)
Multiplicative Seasonal ARIMA Models
155(11)
Long Memory ARMA and Fractional Differencing
166(5)
Threshold Models
171(4)
Regression with Autocorrelated Errors
175(3)
Lagged Regression: Transfer Function Modeling
178(6)
ARCH Models
184(6)
Hilbert Spaces and the Projection Theorem
190(5)
Causal Conditions for ARMA Models
195(2)
Large Sample Distribution of AR Estimators
197(16)
Problems
201(12)
Spectral Analysis and Filtering
Introduction
213(2)
Cyclical Behavior and Periodicity
215(3)
Power Spectrum and Cross Spectrum
218(9)
Linear Filters
227(7)
Discrete Fourier Transform, Periodogram
234(8)
Nonparametric Spectral Estimation
242(9)
Parametric Spectral Estimation
251(7)
Lagged Regression Models
258(5)
Signal Extraction and Optimum Filtering
263(5)
Spectral Analysis of Multidimensional Series
268(2)
Spectral Representation Theorem
270(5)
Large Sample Distribution of Discrete Fourier Transform
275(9)
Complex Multivariate Normal Distribution
284(17)
Problems
289(12)
State-Space and Multivariate ARMAX Models
Introduction
301(11)
Filtering, Smoothing, and Forecasting
312(9)
Maximum Likelihood Estimation
321(8)
Missing Data Modifications
329(4)
Structural Models: Signal Extraction and Forecasting
333(3)
ARMAX Models in State-Space Form
336(3)
Bootstrapping State-Space Models
339(6)
Dynamic Linear Models with Switching
345(13)
Nonlinear and Nonnormal State-Space Models Using Monte Carlo Methods
358(12)
Stochastic Volatility
370(2)
State-Space and ARMAX Models for Longitudinal Data Analysis
372(11)
Further Aspects of Multivariate ARMA and ARMAX Models
383(30)
Problems
404(9)
Statistical Methods in the Frequency Domain
Introduction
413(4)
Spectral Matrices and Likelihood Functions
417(1)
Regression for Jointly Stationary Series
418(9)
Regression with Deterministic Inputs
427(8)
Random Coefficient Regression
435(5)
Analysis of Designed Experiments
440(10)
Discrimination and Cluster Analysis
450(15)
Principal Components, Canonical, and Factor Analysis
465(18)
The Spectral Envelope
483(24)
Dynamic Fourier Analysis and Wavelets
507(22)
Problems
521(8)
References 529(14)
Index 543

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