Although total least squares (TLS) is more rigorous than the weighted least squares (LS) method to estimate the parameters in an errors-in-variables (EIV) model, it is computationally much more complicated than the weighted LS method. For some EIV problems, the TLS and weighted LS methods have been shown to produce practically negligible differences in the estimated parameters. To understand under what conditions we can safely use the usual weighted LS method, we systematically investigate the effects of the random errors of the design matrix on weighted LS adjustment.
Zeng, W., Liu, J., Xu, P., Shen, Y. Effects of errors-in-variables on weighted least squares estimation,
Springer Berlin Heidelberg, 2014, с. 705-716.
Zeng, W., Liu, J., Xu, P., Shen, Y. .
Effects of errors-in-variables on weighted least squares estimation.
: Springer Berlin Heidelberg, 2014, с. 705-716.
Zeng, W., Liu, J., Xu, P., Shen, Y. (2014)
Effects of errors-in-variables on weighted least squares estimation,
: Springer Berlin Heidelberg, с. 705-716
Zeng, W.,
Liu, J.,
Xu, P., &
Shen, Y.
(2014).
Effects of errors-in-variables on weighted least squares estimation. Journal of Geodesy. Springer Berlin Heidelberg 88 (7), с. 705-716.
