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Date: Wed, 31 Oct 2012 12:29:13 +0000 (UTC)
From: "Gilles (JIRA)" <jira@apache.org>
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Subject: [jira] [Commented] (MATH-878) G-Test (Log-Likelihood ratio - LLR
 test) in math.stat.inference
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    [ https://issues.apache.org/jira/browse/MATH-878?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13487709#comment-13487709 ] 

Gilles commented on MATH-878:
-----------------------------

bq. think it is not touching any old CM functionality and as such is no risk. Then why bother to keep GTest call away?

I don't understand what you mean. Making the commit as small as they is just a convenience for reviewing code, now and later. Anyways, Phil is taking care of this report; IIUC, there is no request that you modify your contribution at this point.

bq. If the slightly misleading name of the TestUtils.java is to be changed, than that should be done with a dedicated Issue.

Of course.

                
> 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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