Introduction to Statistical Time Series.

Wayne A. Fuller .

ISBN 10: 0471287156 / ISBN 13: 9780471287155

Edité par John Wiley & Sons, New York, NY, 1976

A propos de cet article

Near Fine in Wraps: shows only very minor indications of use: just a hint of wear to extremities, though the lower front corner tip is very mildly bumped; a thin, faint line of soiling at the top edge and a couple of faint smudges at the fore-edge. The Binding is square and secure; the text is clean. Very close to 'As New'. NOT a Remainder, Book-Club, or Ex-Library. 8vo. 470pp. Wiley Series in (Probability & Mathematical Statistics). First Edition [1971]; Second Printing. Hardback: Lacks DJ. In 1981 I used this book for a graduate seminar that I taught on time series analysis at UC Santa Barbara. The course was successful because I followed the text closely. It presents the material in a rigorous and cogent manner. Fuller balances time domain methods and frequency domain approaches very well and his book is better written and organized than most.

N° de réf. du vendeur 44224

Détails bibliographiques

• Titre: Introduction to Statistical Time Series.
• Éditeur: John Wiley & Sons, New York, NY
• Date de parution: 1976
• Langue: anglais
• ISBN à 10 chiffres: 0471287156
• ISBN à 13 chiffres: 9780471287155
• Reliure: Hardcover
• Édition: First Edition [1971]; Second Printing.
• État de la jaquette: Jaquette
• Catalogues du vendeur:
  • Mathematics

A propos de ce titre

Synopsis

Book by Fuller Wayne A

Quatrième de couverture

The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, and the Kalman filter.

Major topics include:

• Moving average and autoregressive processes
• Introduction to Fourier analysis
• Spectral theory and filtering
• Large sample theory
• Estimation of the mean and autocorrelations
• Estimation of the spectrum
• Parameter estimation
• Regression, trend, and seasonality
• Unit root and explosive time series

To accommodate a wide variety of readers, review material, especially on elementary results in Fourier analysis, large sample statistics, and difference equations, has been included.