On a non-parametric confidence interval for the regression slope
Abstract
We investigate an application of the Tukey's methodology in Theil's regression to obtain a confidence interval for the true slope in the straight line regression model with not necessarily normal errors. This specific approach is implemented since 2005 in a package of the software R; however, without any theoretical background. We illustrate by Monte Carlo simulations, that this methodology, unlike the classical Theil's approach based on Kendall's tau, seriously deflates the true confidence level of the resulting interval. We provide also rigorous proofs in case of four data points (in general) and in case of five data points (under some additional conditions); together with a real life methods usage example in the latter case. Summing up, we demonstrate that one should never combine statistical methods without checking the assumptions of their usage and we also give a warning to the already wide community of R users of Theil's regression from various fields of science.
Keywords
Cite
@article{arxiv.1502.06412,
title = {On a non-parametric confidence interval for the regression slope},
author = {Róbert Tóth and Ján Somorčík},
journal= {arXiv preprint arXiv:1502.06412},
year = {2016}
}
Comments
Changes in Version 2: (1) added references to the usage of the R package "mblm" in other sciences; (2) expanded Abstract and Introduction; (3) updated affiliation of the first author. Changes in Version 3 : (1) Condition 2 itroduced at a later stage; (2) true confidence levels of the Theil's CI added to Table 1. Changes in Version 4: section `A real life example' added