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List:       jakarta-commons-dev
Subject:    [jira] [Commented] (MATH-1233) Uncommon wilcoxon signed-rank p-values
From:       "Rob Tompkins (JIRA)" <jira () apache ! org>
Date:       2017-04-30 18:37:04
Message-ID: JIRA.12837832.1434373546000.87849.1493577424124 () Atlassian ! JIRA
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    [ https://issues.apache.org/jira/browse/MATH-1233?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15990332#comment-15990332 \
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Rob Tompkins commented on MATH-1233:
------------------------------------

After some reading here, the assumptions on the data given are:

# Data are paired and come from the same population.
# Each pair is chosen randomly and independently.
# The data are measured at least on an ordinal scale (i.e., they cannot be nominal).

I wonder if the two input vectors are the same, if we are not violating 3. I \
generally agree here that the same vector should be treated in its own way. I would \
think that we may want to throw an exception. The only question then becomes \
performance in nature, in that, is doing array equality at the beginning of the \
procedure valuable enough that we are willing to do it every time despite the _O(n)_ \
performance hit? Or do we simply document the fact that we'll not give reliable \
results when the vectors are the same.

> Uncommon wilcoxon signed-rank p-values
> --------------------------------------
> 
> Key: MATH-1233
> URL: https://issues.apache.org/jira/browse/MATH-1233
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 3.5
> Reporter: Icaro Cavalcante Dourado
> Fix For: 4.0
> 
> Attachments: MATH-1233-test.patch
> 
> 
> This implementation in WilcoxonSignedRankTest looks weird. For equal vectors, the \
> correct pValue should be 1, because it is the probability of the vectors to come \
> from same population. On the opposite, this implementation returns ~0 for equal \
> vectors. So we need to analyze the returned pValue > significanceLevel to reject H0 \
> hypothesis, while in R and many others tools we perform the opposite: pValue <= \
> significanceLevel gives us an argument to reject null hypothesis.



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