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https://issues.apache.org/jira/browse/MATH-878?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Radoslav Tsvetkov updated MATH-878:
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Attachment: MATH-878_gTest_15102012.patch
Hi Ted,
Signed Root LLR is a really good idea! I added the test (also in TestUtilsTest)
And some comment on signed rLLR:
In some cases of unexpectedly small similar p1 and p2 values
* or large anomalies of k11, ... counts it is desired to
* get additional information on the rate trough signed root LLR.
*
* Signed root LLR has two advantages over the basic LLR:
* a) it is positive where k11 is bigger than expected, negative where it
is
* lower. This resolves your current problem.
* b) if there is no difference it is asymptotically normally distributed.
* This allows people to talk about "number of standard deviations" which
is a
* more common frame of reference than the chi^2 distribution.
*
* See Discussions at: ....
> G-Test (Log-Likelihood ratio - LLR test) in math.stat.inference
> ---------------------------------------------------------------
>
> Key: MATH-878
> URL: https://issues.apache.org/jira/browse/MATH-878
> Project: Commons Math
> Issue Type: New Feature
> Affects Versions: 3.1, 3.2, 4.0
> Environment: Netbeans
> Reporter: Radoslav Tsvetkov
> Labels: features, test
> Fix For: 3.1
>
> Attachments: MATH-878_gTest_12102012.patch,
> MATH-878_gTest_15102012.patch, vcs-diff16294.patch
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> 1. Implementation of G-Test (Log-Likelihood ratio LLR test for independence
> and goodnes-of-fit)
> 2. Reference: http://en.wikipedia.org/wiki/G-test
> 3. Reasons-Usefulness: G-tests are tests are increasingly being used in
> situations where chi-squared tests were previously recommended.
> The approximation to the theoretical chi-squared distribution for the G-test
> is better than for the Pearson chi-squared tests. In cases where Observed
> >2*Expected for some cell case, the G-test is always better than the
> chi-squared test.
> For testing goodness-of-fit the G-test is infinitely more efficient than the
> chi squared test in the sense of Bahadur, but the two tests are equally
> efficient in the sense of Pitman or in the sense of Hodge and Lehman.
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