© 2000

Asymptotic Theory of Statistical Inference for Time Series

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

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 1-29
  3. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 31-49
  4. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 51-165
  5. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 167-306
  6. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 307-383
  7. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 385-476
  8. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 477-535
  9. Masanobu Taniguchi, Yoshihide Kakizawa
    Pages 537-618
  10. Back Matter
    Pages 619-662

About this book


There has been much demand for the statistical analysis of dependent ob­ servations in many fields, for example, economics, engineering and the nat­ ural sciences. A model that describes the probability structure of a se­ ries of dependent observations is called a stochastic process. The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. We deal with a wide variety of stochastic processes, for example, non-Gaussian linear processes, long-memory processes, nonlinear processes, orthogonal increment process­ es, and continuous time processes. For them we develop not only the usual estimation and testing theory but also many other statistical methods and techniques, such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of differential geometry, large deviation principle, and saddlepoint approximation. Because it is d­ ifficult to use the exact distribution theory, the discussion is based on the asymptotic theory. Optimality of various procedures is often shown by use of local asymptotic normality (LAN), which is due to LeCam. This book is suitable as a professional reference book on statistical anal­ ysis of stochastic processes or as a textbook for students who specialize in statistics. It will also be useful to researchers, including those in econo­ metrics, mathematics, and seismology, who utilize statistical methods for stochastic processes.


Analysis of Stochastic Processes Inference for Stochastic Processes Ornstein-Uhlenbeck process Statistical Inference Stochastic processes Time series diffusion process statistics stochastic process

Authors and affiliations

  1. 1.Department of Mathematical Science Faculty of Engineering ScienceOsaka UniversityToyonakaJapan
  2. 2.Faculty of EconomicsHokkaido UniversitySapporoJapan

Bibliographic information

  • Book Title Asymptotic Theory of Statistical Inference for Time Series
  • Authors Masanobu Taniguchi
    Yoshihide Kakizawa
  • Series Title Springer Series in Statistics
  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 2000
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Hardcover ISBN 978-0-387-95039-6
  • Softcover ISBN 978-1-4612-7028-7
  • eBook ISBN 978-1-4612-1162-4
  • Series ISSN 0172-7397
  • Edition Number 1
  • Number of Pages XVII, 662
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Statistical Theory and Methods
    Probability Theory and Stochastic Processes
  • Buy this book on publisher's site
Industry Sectors
Health & Hospitals
IT & Software
Consumer Packaged Goods
Oil, Gas & Geosciences
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment


From the reviews:


"It is valuable both as an advanced graduate level text and as a reference for researchers?he book can be most strongly recommended."