Common factor detection and estimation
type: Item
instance of: scholarly article
title: Common factor detection and estimation
zbMATH Open document ID: 0881.93076
DOI: 10.1016/S0005-1098(96)00248-8
author: Torsten Söderström
author: Petre Stoica
published in: Automatica
publication date: 1997-07-17T00:00:00Z
review text: The authors consider the greatest common factor detection and estimation (CODE) of two polynomials of degrees \(n\alpha\) and \(n\beta\) respectively, the coefficients of which are assumed to have a normal distribution with a consistently estimable covariance matrix. First, using a large-sample maximum likelihood approach the common factor test criterion is obtained. Then, under setting \(\widehat{nc}=\min(n\alpha, n\beta)\), the minimization of the criterion is carried out with respect to the coefficients of the common factor and the deflated polynomials. Then, a \(\chi^2\)-based detection procedure is performed. If it fails then retry the procedure with setting \(\widehat{nc}= \widehat{nc}-1\) until success. This noniterative CODE-algorithm can also be used for estimating the orders and parameters of the ARMAX models. Numerical examples are included to indicate that the performance of the proposed methodology is rather good.
reviewed by: Chengxun Wu
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93B30
zbMATH DE Number: 1036549
zbMATH Keywords: hypothesis testing
zbMATH Keywords: greatest common factor detection and estimation
zbMATH Keywords: maximum likelihood
MaRDI profile type: Publication
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full work available at URL: s0005-1098(96)00248-8
OpenAlex ID: W1970459133
description: scientific article; zbMATH DE number 1036549