Publication at Faculty of Mathematics and Physics |
2013
Abstract
The solution to the errors-in-variables (EIV) problem computed through total least squares (TLS) is highly nonlinear. Because of this, many statistical procedures for constructing confidence intervals and testing hypotheses cannot be applied.
One possible solution to this dilemma is bootstrapping. A nonparametric bootstrap technique could fail.
The proper nonparametric bootstrap procedure is provided and its correctness proved. On the other hand, a residual bootstrap is not valid and suitable in this case.
The results are illustrated through a simulation study. An application of this approach to calibration data is presented.