On the recursive solution of the normal equations of bilateral multivariate autoregressive models

Author

Choi, ByoungSeon

Author_Institution

Dept. of Stat., Stanford Univ., CA, USA

Volume

47

Issue

5

fYear

1999

fDate

5/1/1999 12:00:00 AM

Firstpage

1388

Lastpage

1390

Abstract

A multivariate version of the bilateral autoregressive (AR) model is proposed, and a recursive algorithm is presented to solve the normal equations of the bilateral multivariate AR models. The recursive algorithm is computationally efficient and easy to implement as a computer program. The recursive algorithm is useful for identifying and smoothing not only bilateral multivariate AR processes but multidimensional multivariate AR processes and multivariate spatio-temporal processes as well

Keywords

Toeplitz matrices; autoregressive processes; recursive estimation; signal processing; smoothing methods; Toeplitz matrix; bilateral multivariate autoregressive models; computationally efficient algorithm; computer program; multidimensional multivariate AR process; multivariate spatio-temporal process; normal equations; process identification; process smoothing; recursive algorithm; recursive solution; Equations; Least squares approximation; Multidimensional systems; Recursive estimation; Signal processing algorithms; Smoothing methods; Statistics;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.757227

Filename

757227