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From "Gilles (JIRA)" <>
Subject [jira] [Commented] (MATH-878) G-Test (Log-Likelihood ratio - LLR test) in math.stat.inference
Date Tue, 30 Oct 2012 16:08:12 GMT


Gilles commented on MATH-878:

In fact, I thought (misread) that it was the "TestUtils" class from the "test" part of the
We might think of making that name (from "main" part, I mean) less ambiguous (e.g. something
like "StatisticTestsUtils").

Still, two commits are always clearer, if they can be made independently. No? ;)
If you find that one is fine, no problem for me.

> G-Test (Log-Likelihood ratio - LLR test) in math.stat.inference
> ---------------------------------------------------------------
>                 Key: MATH-878
>                 URL:
>             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,
>   Original Estimate: 24h
>  Remaining Estimate: 24h
> 1. Implementation of G-Test (Log-Likelihood ratio LLR test for independence and goodnes-of-fit)
> 2. Reference:
> 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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