![Sample our Bioscience journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5925/TOC-Subject-Sample-2021-Bioscience.jpg"})
Abstract
The solution to the errors-in-variables problem computed through total least squares 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. Here, the proper nonparametric bootstrap procedure is provided and its correctness is 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.
Acknowledgements
The author thanks J. Antoch for his valuable support and the anonymous reviewer for the suggestions that improved this paper. This paper was written with the support of the Czech Science Foundation project No. P402/12/G097 ‘DYME – Dynamic Models in Economics’.
Notes
The proofs of the theorems contained within this paper may be found in the Appendix.