Asymptotic variance–covariance matrices for the linear structural model

Gillard, Jonathan William ORCID: https://orcid.org/0000-0001-9166-298X 2011. Asymptotic variance–covariance matrices for the linear structural model. Statistical Methodology 8 (3) , pp. 291-303. 10.1016/j.stamet.2010.12.002

Full text not available from this repository.

Abstract

In recent years there has been renewed interest in errors in variables regression models, where there are errors in the predictor variables as well as the dependent variable. Despite recent advances, the theory of the simple linear errors in variables model does not yet match the well known methodology of simple linear regression. This paper fills one of these gaps by presenting results for the straight line errors in variables model that will enable a practitioner to estimate not only the parameters of the model but also the approximate variances of these estimates. Attention is therefore focused on the variances of the model and the extraction of estimates. The presentation adopts a method of moments approach, but connections are made with the method of least squares and the maximum likelihood approach

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	Errors in variables regression; Measurement error; Linear structural model; Method of moments; Variance–covariance matrix
Publisher:	Elsevier
ISSN:	1572-3127
Last Modified:	19 Oct 2022 09:39
URI:	https://orca.cardiff.ac.uk/id/eprint/21776

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item