Although total least squares has been substantially investigated theoretically and widely applied in practical applications, almost nothing has been done to simultaneously address the estimation of parameters and the errors-in-variables (EIV) stochastic model. We prove that the variance components of the EIV stochastic model are not estimable, if the elements of the random coefficient matrix can be classified into two or more groups of data of the same accuracy. This result of inestimability is surprising as it indicates that we have no way of gaining any knowledge on such an EIV stochastic model.
Liu, J., Xu, P. Variance components in errors-in-variables models: estimability, stability and bias analysis,
Springer Berlin Heidelberg, 2014, с. 719-734.
Liu, J., Xu, P. .
Variance components in errors-in-variables models: estimability, stability and bias analysis.
: Springer Berlin Heidelberg, 2014, с. 719-734.
Liu, J., Xu, P. (2014)
Variance components in errors-in-variables models: estimability, stability and bias analysis,
: Springer Berlin Heidelberg, с. 719-734
Liu, J., & Xu, P.
(2014).
Variance components in errors-in-variables models: estimability, stability and bias analysis. Journal of Geodesy. Springer Berlin Heidelberg 88 (8), с. 719-734.
