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Time Series: Theory and Methods

  • Peter J. Brockwell
  • Richard A. Davis

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Peter J. Brockwell, Richard A. Davis
    Pages 1-41
  3. Peter J. Brockwell, Richard A. Davis
    Pages 42-76
  4. Peter J. Brockwell, Richard A. Davis
    Pages 77-111
  5. Peter J. Brockwell, Richard A. Davis
    Pages 112-158
  6. Peter J. Brockwell, Richard A. Davis
    Pages 159-190
  7. Peter J. Brockwell, Richard A. Davis
    Pages 191-210
  8. Peter J. Brockwell, Richard A. Davis
    Pages 211-230
  9. Peter J. Brockwell, Richard A. Davis
    Pages 231-264
  10. Peter J. Brockwell, Richard A. Davis
    Pages 265-319
  11. Peter J. Brockwell, Richard A. Davis
    Pages 320-390
  12. Peter J. Brockwell, Richard A. Davis
    Pages 391-446
  13. Peter J. Brockwell, Richard A. Davis
    Pages 447-498
  14. Back Matter
    Pages 499-519

About this book

Introduction

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 tech­ 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.

Keywords

estimator likelihood statistics time series

Authors and affiliations

  • Peter J. Brockwell
    • 1
  • Richard A. Davis
    • 1
  1. 1.Department of StatisticsColorado State UniversityFort CollinsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4899-0004-3
  • Copyright Information Springer-Verlag New York 1987
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4899-0006-7
  • Online ISBN 978-1-4899-0004-3
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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