Title :
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
fDate :
5/1/1999 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
