Publisher:

Chapman & Hall/CRC

Number of Pages:

540

Price:

99.95

ISBN:

9781439818374

Date Received:

Thursday, November 10, 2011

Reviewable:

Reviewer Email Address:

Series:

Statistics: Textbooks and Monographs

Publication Date:

2012

Format:

Hardcover

Audience:

Category:

Textbook

**Stationary Time Series**Time Series

Stationary Time Series

Autocovariance and Autocorrelation Functions for Stationary Time Series

Estimation of the Mean, Autocovariance, and Autocorrelation for Stationary Time Series

Power Spectrum

Estimating the Power Spectrum and Spectral Density for Discrete Time Series

Time Series Examples

Stationary General Linear Processes

Wold Decomposition Theorem

Filtering Applications

Autoregressive Processes

Autoregressive–Moving Average Processes

Visualizing Autoregressive Components

Seasonal ARMA(

Generating Realizations from ARMA(

Transformations

ARCH and GARCH Models

ARIMA(p,d,q) and ARUMA(

Multiplicative Seasonal ARUMA(

Random Walk Models

G-Stationary Models for Data with Time-Varying Frequencies

Box–Jenkins Forecasting for ARMA(

Properties of the Best Forecast X

pi-Weight Form of the Forecast Function

Forecasting Based on the Difference Equation

Eventual Forecast Function

Probability Limits for Forecasts

Forecasts Using ARUMA(

Forecasts Using Multiplicative Seasonal ARUMA Models

Forecasts Based on Signal-plus-Noise Models

Preliminary Estimates

Maximum Likelihood Estimation of ARMA(

Backcasting and Estimating

Estimation Examples Using Data

ARMA Spectral Estimation

ARUMA Spectral Estimation

Model Identification for Stationary ARMA Models

Model Identification for Nonstationary ARUMA(

Model Identification Based on Pattern Recognition

Stationarity versus Nonstationarity

Signal-plus-Noise versus Purely Autocorrelation-Driven Models

Checking Realization Characteristics

Comprehensive Analysis of Time Series Data: A Summary

Stationary Multivariate Time Series

Multivariate (Vector) ARMA Processes

Nonstationary VARMA Processes

Testing for Association between Time Series

State-Space Models

Proof of Kalman Recursion for Prediction and Filtering

Fractional Difference and FARMA Models

Gegenbauer and GARMA Processes

Parameter Estimation and Model Identification

Forecasting Based on the

Modeling Atmospheric CO

Wavelets

Methods That Localize the ‘‘Spectrum’’ in Time

Wavelet Analysis

Wavelet Packets

Concluding Remarks on Wavelets

Appendix: Mathematical Preliminaries for This Chapter

M-Stationary Processes

G(λ)-Stationary Processes

Linear Chirp Processes

Concluding Remarks

Index

Publish Book:

Modify Date:

Friday, March 30, 2012

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