DIF methods may be divided into parametric methods, requiring distributional assumptions of a particular model, and non-parametric methods that are distribution-free.
The CID does not require distributional and functional assumptions regarding the data and is useful for analyzing noisy microarray data.
In our opinion the permutation test is more appropriate for this data since it does not require distributional assumptions and directly tests the hypothesis of interest, so permutation test p-values will be used in the text, although t-test p-values are also reported for interested readers.
Conventional methods for establishing confidence intervals require distributional assumptions which are not available in this case.
This approach does not require distributional assumptions (e.g., normality) and the confidence limits are not constrained to be symmetrical.
The paired Monte Carlo permutation test [ 65, 66] based on the observation-level scores provides a convenient approach, as unlike the paired t-test it does not require distributional assumptions (e.g. normality of individual scores) or trust in asymptotic behavior.
Our approach is to consider only heuristic methods here with the exception of Velicer's Partial Correlation test, which is a statistical method that does not require distributional assumptions nor is it computationally intensive.
Flat prior distributions were assigned to G 0, H 0 and R 0. The multivariate normal distribution requires no distributional specification of λ i in equation (2), because λ i = 1 for all i = 1, 2,..., n.
The IV estimation method for linear regressions has been preferred to a IV probit estimator also in the case of binary outcomes because it requires fewer distributional assumptions and consistently estimates average treatment effects in the case of binary endogenous variables (Newey 1987; Angrist 1991 .31.
We decided to utilize bootstrapping as it requires less distributional assumptions and utilizes a larger sample size as compared to other methods (split sample or jackknife).
