The results from Table 3 and Appendix 1 (Table 4) show that Inverse chi test and Tippett's test are very robust to dependency in p-values with well controlled Type I error rates.
The results from the simulation study showed that mean estimates became substantially biased even at relatively weak dependencies between follow-up variables and attrition, whereas estimates of associations between variables were more robust to dependencies between attrition and study variables.
To obtain robust results, avoiding dependency on a particular execution of the algorithm, this article introduces the following modification to identify the main clusters of the network in a robust way.
To obtain the adjusted association between functional dependency (dependent variable) and the TSNs (independent variables), a multiple regression (Poisson regression with robust variance) analysis was performed using the forward selection method.
We estimated GRM with and without the flagged local dependent questions (residual correlations higher than 0.2) to see if results were robust to question dependencies [ 22].
The general patterns of these dependencies are robust.
Besides that, more robust and meaningful spatial dependencies were observed among stream health measures and environmental justice parameters at different spatial levels.
*OR=odds ratio based on robust clustering accounting for dependencies within data.
Key-dependency schemes used in the construction of robust hashes include key-dependent transformations [1, 4, 12], pseudorandom permutation of the data [13], randomized statistical features [8 10], and randomized quantization/clustering [14].
In addition, the dependency probability is robust in calculation and can be easily implemented in practice.
