Consistency of modified LS estimation method for identifying 2-D noncausal SAR model parameters

Author

Zhao, Ping-ya ; Litva, John

Author_Institution

Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada

Volume

40

Issue

2

fYear

1995

fDate

2/1/1995 12:00:00 AM

Firstpage

316

Lastpage

320

Abstract

Least squares (LS) and maximum likelihood (ML) are the two main methods for parameter estimation of two-dimensional (2D) noncausal simultaneous autoregressive (SAR) models. ML is asymptotically consistent and unbiased but computationally unattractive. On the other hand, conventional LS is computationally efficient but does not produce accurate parameter estimates for noncausal models. Recently, Zhao-Yu (1993) proposed a modified LS estimation method and was shown to be unbiased. In this paper we prove that, under certain assumptions, the method introduced by Zhao-Yu is also consistent

Keywords

autoregressive processes; least squares approximations; maximum likelihood estimation; parameter estimation; 2D noncausal simultaneous autoregressive models; identification; least squares estimation; maximum likelihood estimation; parameter estimation; Autoregressive processes; Computational complexity; Kalman filters; Maximum likelihood estimation; Modeling; Multidimensional systems; Parameter estimation; Recursive estimation; Signal processing; Two dimensional displays;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.341801

Filename

341801