We present a two-dimensional linear regression model where both variables are subject to error. We discuss a model where one variable of each pair of observables is repeated. We suggest two methods to construct consistent estimators: the maximum likelihood method and the method which applies variance components theory. We study asymptotic properties of these estimators. We prove that the asymptotic variances of the estimators of regression slopes for both methods are comparable.
@article{bwmeta1.element.bwnjournal-article-zmv27i3p335bwm,
author = {Anna Czapkiewicz and Antoni Dawidowicz},
title = {The two-dimensional linear relation in the errors-in-variables model with replication of one variable},
journal = {Applicationes Mathematicae},
volume = {27},
year = {2000},
pages = {335-342},
zbl = {0990.62053},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-zmv27i3p335bwm}
}
Czapkiewicz, Anna; Dawidowicz, Antoni. The two-dimensional linear relation in the errors-in-variables model with replication of one variable. Applicationes Mathematicae, Tome 27 (2000) pp. 335-342. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-zmv27i3p335bwm/
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