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Full-Text Articles in Social and Behavioral Sciences
On Polynomial Transformations For Simulating Multivariate Non-Normal Distributions, Todd C. Headrick
On Polynomial Transformations For Simulating Multivariate Non-Normal Distributions, Todd C. Headrick
Journal of Modern Applied Statistical Methods
Procedures are introduced and discussed for increasing the computational and statistical efficiency of polynomial transformations used in Monte Carlo or simulation studies. Comparisons are also made between polynomials of order three and five in terms of (a) computational and statistical efficiency, (b) the skew and kurtosis boundary, and (c) boundaries for Pearson correlations. It is also shown how ranked data can be simulated for specified Spearman correlations and sample sizes. Potential consequences of nonmonotonic transformations on rank correlations are also discussed.
A Power Comparison Of Robust Test Statistics Based On Adaptive Estimators, H. J. Keselman, Rand R. Wilcox, James Algina, Abdul R. Othman
A Power Comparison Of Robust Test Statistics Based On Adaptive Estimators, H. J. Keselman, Rand R. Wilcox, James Algina, Abdul R. Othman
Journal of Modern Applied Statistical Methods
Seven test statistics known to be robust to the combined effects of nonnormality and variance heterogeneity were compared for their sensitivity to detect treatment effects in a one-way completely randomized design containing four groups. The six Welch-James-type heteroscedastic tests adopted either symmetric or asymmetric trimmed means, were transformed for skewness, and used a bootstrap method to assess statistical significance. The remaining test, due to Wilcox and Keselman (2003), used a modification of the well-known one-step M-estimator of central tendency rather than trimmed means. The Welch-James-type test is recommended because for nonnormal data likely to be encountered in applied research settings …