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ANOVA-type statistics are suggested for both small and moderate sample sizes.
For small and moderate sample sizes, a permutation procedure is suggested to assess significance.
In a simulation study we found out that our tests achieve the nominal level and already have excellent power for small to moderate sample sizes.
For moderate sample sizes, the bootstrap approach provides an improvement to existing methods in particular for situations with nonnormal data and heterogeneous covariance matrices in unbalanced designs.
The paper studies explicitly defined interval estimation of the difference in proportions arising from independent binomial distributions for small to moderate sample sizes.
Given the moderate sample sizes, only the primary variables of interest described above were included in the regression models; no additional covariates were included.
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This means that we are in the realm of moderate sample size and moderate dimensionality, for which VB is accurate (see Section 3.3).
Several asymptotic results concerning the proposed procedures are given along with simulation results to assess their performance in moderate sample size situations.
Several asymptotic results concerning the proposed procedure are given along with simulation results to assess its performance in moderate sample size situations.
The simulation study shows that for a moderate sample size and selection probability, the IPW estimator and AIPW estimator perform better than the complete-case estimator.
For moderate sample size, the situation is in between the two extreme cases, and the weight function is selected by minimizing a variant of the information criterion derived as an estimate of the expected loss.
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