Author | Brockwell, Peter J. author |
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Title | Time Series: Theory and Methods [electronic resource] / by Peter J. Brockwell, Richard A. Davis |

Imprint | New York, NY : Springer New York : Imprint: Springer, 1987 |

Connect to | http://dx.doi.org/10.1007/978-1-4899-0004-3 |

Descript | XIV, 520 p. online resource |

SUMMARY

We have attempted in this book to give a systematic account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It has been used both at the M. S. level, emphasizing the more practical aspects of modelling, and at the Ph. D. level, where the detailed mathematical derivations of the deeper results can be included. Distinctive features of the book are the extensive use of elementary Hilbert space methods and recursive prediction techniques based on innovations, use of the exact Gaussian likelihood and AIC for inference, a thorough treatment of the asymptotic behavior of the maximum likelihood estimators of the coefficients of univariate ARMA models, extensive illustrations of the techยญ niques by means of numerical examples, and a large number of problems for the reader. The companion diskette contains programs written for the IBM PC, which can be used to apply the methods described in the text

CONTENT

Stationary Time Series -- Hilbert Spaces -- Stationary ARMA Processes -- The Spectral Representation of a Stationary Process -- Prediction of Stationary Processes -- Asymptotic Theory -- Estimation of the Mean and the Autocovariance Function -- Estimation for ARMA Models -- Model Building and Forecasting with ARIMA Processes -- Inference for the Spectrum of a Stationary Process -- Multivariate Time Series -- Further Topics

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