Consistent estimation for an errors-in-variables model based on constrained total least squares problems

In this paper, we construct a natural errors-in-variables regression model corresponding to the total least squares (TLS) problem with linear equations constraints and prove strong consistency of the estimation. The presented asymptotic theory in the statistical sense is a generalization of the consistency analysis of an estimation with the use of ordinary total least squares for basic errors-in-variables regression models.