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From "Phil Steitz (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MATH-431) New tests: Wilcoxon signed-rank test and Mann-Whitney U
Date Sat, 06 Nov 2010 16:07:52 GMT

    [ https://issues.apache.org/jira/browse/MATH-431?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12929054#action_12929054
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Phil Steitz commented on MATH-431:
----------------------------------

+1 for including both of these tests.  Then on to MATH-228

Interesting approach for the exact algorithm for Wilcoxon.  If we stay with this, we should
ack the original author of the algorithm in the javadoc.  Looks OK to use.  Regarding the
difference from R, what I usually do in this case is look at the R sources to try to explain
the difference.  Most likely in this case, what is going on is they are using a different
estimation algorithm for small n or treating ties differently.  The ranking options that we
use were largely adapted from R, so if that is the problem, it should be easy to test.  We
need to convince ourselves that ours is better or at least a legitimate alternative.  I will
take a close look this evening, but it looks like the algorithm you are using should be exact.
 If we can't reconcile the difference with R, it would be good to find a way to validate correct
functioning of the algorithm by manufacturing reference data with known p.

> New tests: Wilcoxon signed-rank test and Mann-Whitney U
> -------------------------------------------------------
>
>                 Key: MATH-431
>                 URL: https://issues.apache.org/jira/browse/MATH-431
>             Project: Commons Math
>          Issue Type: New Feature
>            Reporter: Mikkel Meyer Andersen
>            Assignee: Mikkel Meyer Andersen
>            Priority: Minor
>         Attachments: MannWhitneyUTest.java, MannWhitneyUTestImpl.java, WilcoxonSignedRankTest.java,
WilcoxonSignedRankTestImpl.java
>
>   Original Estimate: 4h
>  Remaining Estimate: 4h
>
> Wilcoxon signed-rank test and Mann-Whitney U are commonly used non-parametric statistical
hypothesis tests (e.g. instead of various t-tests when normality is not present).

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