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Date: Tue, 30 Oct 2012 15:28:12 +0000 (UTC)
From: "Phil Steitz (JIRA)" <jira@apache.org>
To: issues@commons.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=13486940#comment-13486940 ] 

Phil Steitz commented on MATH-878:
----------------------------------

I have this just about ready to be committed. I don't understand exactly what you mean by separating the calls in, Gilles? TestUtils is just a container for static convenience methods executing significance tests.  It is appropriate to include GTests there.  Do you mean these should be done in two separate commits?  I guess that is OK, but both should be associated with this issue.  I can commit the code in two commits, if that is what you mean.  
                
> 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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