[BioC] Wilcoxon test [was loged data or not loged previous to use normalize.quantile]

[Ramon Diaz-Uriarte]

> Pollard and van der Laan have investigated resampling tests under
> variance inequality. Although permutation methods do not give exact
> control of type 1 errors for unequal variances (or non independently
> identically distributed data in general), they control this error
> asymptotically for balanced sample sizes. A simulation study in their
> paper confirmed that, so as long as the sample sizes are balanced you
> might not be that bad off with using a permutation method. Otherwise
> bootstrapping (within group based on the residuals) should be preferred
>

This is probably cited in Pollard & van der Laan, and is not directly 
concerned with sets of a bunch of tests, but the 1990 paper in JASA by J. 
Romano ("On the behavior of randomization tests without a group invariance 
assumption", JASA, 85: 686--692) shows that the permutation test for 
differences in means is asymptotically valid only if variances are equal or 
sample sizes are approx. the same (which are the same conditions that need to 
hold for the usual two-sample t-test). Interestingly, however, when testing 
the medians the permutation test is generally invalid (true alpha far from 
nominal alpha) even when sample sizes are the same. 

R.


-- 
Ramón Díaz-Uriarte
Bioinformatics Unit
Centro Nacional de Investigaciones Oncológicas (CNIO)
(Spanish National Cancer Center)
Melchor Fernández Almagro, 3
28029 Madrid (Spain)
Fax: +-34-91-224-6972
Phone: +-34-91-224-6900

http://ligarto.org/rdiaz
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