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https://issues.apache.org/jira/browse/MATH-878?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490276#comment-13490276
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Phil Steitz commented on MATH-878:
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Implementation code committed in r1405620.
I made no material changes - just javadoc, making a few variables final that
could be final and incorporating the MATH-885 changes (externalizing array
argument checks) I also added a few more tests.
I am still working on the TestUtils changes. Name change there will have to
wait until 4.0 if we decide to do it. I am ambivalent, as the package name
.inference is what you would end up logically adding - i.e., InferenceTestUtils
- but that would be redundant. I will add a reference to Ted's paper and other
discussion in the User Guide.
I am also wondering whether it may be better to make the entropy methods public
and move them to StatUtils.
> 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, MATH-878_gTest_26102012.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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