To assess the effect of non-normality on the properties of the proposed test statistics we generated data from a log-normal distribution, and evaluated the performance of the four procedures for 2000 simulated datasets.
In this simulation we compare estimation of standard deviation under these methods in S2 (that is, when xmin, xQ1, xmed, xQ3, xmax, and n are available) and examine the effect of violation of normality using the log-normal distribution.
The patient sample we studied is sufficiently large that the potential deleterious effect of non-normality on the significance of the path coefficients may be offset.
The effect of non-normality on the performance of proposed chart is assessed using Gamma, Student's t and Logistic distributions.
In this note, we address the effect of semi-normality on the expressive power of defaults, using a classification method based on polynomial, faithful, and modular (PFM) translation functions.
Further, the results might be also affected even Bollen-Stine bootstrap was used to manage the effect of deviation form normality, so the usage of polychoric correlations would be an alternative.
A phenomenological elastic viscoplastic material model is applied, using an anisotropic yield criterion, and in each case analyzed the effect of non-normality is compared with results for the standard normality flow rule.
In this study, we use simulations to assess the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) with variable association tests to detect 2-locus epistasis signals.
In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects.
In order to assess the effect of possible non-normality on the analyses, they were repeated using non-parametric 2-factor orthogonal Kruskal-Wallis tests [ 11].
