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» » Elements of Multivariate Time Series Analysis (Springer Series in Statistics)
Elements of Multivariate Time Series Analysis (Springer Series in Statistics) e-book

Author:

Gregory C Reinsel

Language:

English

Category:

Math

Subcategory:

Mathematics

ePub size:

1517 kb

Other formats:

lrf mbr docx doc

Rating:

4.9

Publisher:

Springer-Verlag; 2nd Corr Print ed. edition (December 1993)

Pages:

277

ISBN:

3540940634

Elements of Multivariate Time Series Analysis (Springer Series in Statistics) e-book

by Gregory C Reinsel


This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not . Vector ARMA Time Series Models and Forecasting.

This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others. Show all. Table of contents (7 chapters). Vector Time Series and Model Representations.

Time Series Analysis: Univariate and Multivariate Methods. Series: Springer Series in Statistics. Paperback: 358 pages. ISBN-10: 9780387406190. New Introduction to Multiple Time Series Analysis.

Библиографические данные. Elements of Multivariate Time Series Analysis Springer Series in Statistics, ISSN 0172-7397.

Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series.

This book is concerned with the analysis of multivariate time series data. continued aftu index). Elements of Multivariate Time Series Analysis With 11 Illustrations. Such data might arise in business and economics, engineering, geophysical sciences, agriculture, and many other fields. Springer-Verlag New York Berlin Heidelberg London Paris Tokyo Hong Kong Barcelona Budapest.

This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis . Springer Science & Business Media, Dec 6, 2012 - Mathematics - 263 pages. The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years.

Start by marking Elements of Multivariate Time Series Analysis (Springer Series in Statistics) as Want to Read .

Start by marking Elements of Multivariate Time Series Analysis (Springer Series in Statistics) as Want to Read: Want to Read savin. ant to Read.

Springer Texts in Statistics. Jonathan . ryer Kung-Sik Chan. Time Series Analysis. Athreya/Lahiri: Measure Theory and Probability Theory Bilodeau/Brenner: Theory of Multivariate Statistics Brockwell/Davis: An Introduction to Time Series and Forecasting Carmona: Statistical Analysis of Financial Data in S-PLUS Chow/Teicher: Probability Theory: Independence, Interchangeability, Martingales, 3rd ed. Christensen: Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization, 2nd ed. Christensen: Log-Linear Models and Logistic Regression, 2nd ed.

This book is concerned with the analysis of multivariate time series data. Such data might arise in business and economics, engineering, geophysical sciences, agriculture, and many other fields

This book is concerned with the analysis of multivariate time series data. The book presupposes a familiarity with univariate time series as might be gained from one semester of a graduate course, but it is otherwise self-contained.

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This study is devoted to the analysis of multivariate time series data. Such data might arise in business and economics, engineering, geophysical sciences, agriculture, and many other fields. The emphasis is on providing an account of the basic concepts and methods which are useful in analyzing such data. The book presupposes a familiarity with univariate time series as might be gained from one term of a graduate course, but it is otherwise self-contained. It covers the basic topics such as autocovariance matrices of stationary processes, vector ARMA models and their properties, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and associated likelihood ratio testing procedures for model building. In addition, it presents more advanced topics and techniques including reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate nonstationary unit root models and co-integration structure, and state-space models and Kalman flltering techniques.

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