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https://issues.apache.org/jira/browse/MATH-228?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Phil Steitz updated MATH-228:
-----------------------------
Fix Version/s: (was: 3.0)
3.1
Moving to 3.1, awaiting patch.
> Feature request for one and two sample Kolmogorov-Smirnov test as well as
> Lilliefors test
> -----------------------------------------------------------------------------------------
>
> Key: MATH-228
> URL: https://issues.apache.org/jira/browse/MATH-228
> Project: Commons Math
> Issue Type: New Feature
> Affects Versions: 1.2
> Environment: All
> Reporter: Anirban Basu
> Priority: Minor
> Fix For: 3.1
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> It would be very helpful to have implementations of one and two sample
> [Kolmogorov-Smirnov
> test|http://en.wikipedia.org/wiki/Kolmogorov-Smirnov_test] as well as
> [Lilliefors test|http://en.wikipedia.org/wiki/Lilliefors_test] with
> MATLAB-style results in future versions of Commons Math.
> For example, Lilliefors test on sample data:
> sampleVector = [0.0033413022337048857, 0.008527692135731013,
> -0.004902763950955454, 0.033018433100296396, -0.020495504044139023,
> 0.003978726052913162, 0.003847972673931109, 0.009160477945515444,
> -0.011113437653216639, -0.01164235145079795, 0.017180306607011864,
> -0.01818483009998717, -0.010479811709006803, -0.033991339307749,
> -0.007057160031600951, -1.2398497120424956E-4, 0.0026913151777877564,
> 0.03580425341677764, -0.006404370278251359, 0.007579083257585828,
> -0.005912037207256193, 0.01241830354576745, -0.0012524631744377235,
> -0.005900927958040758, 0.0028847985848513558, 0.005313417226899042,
> 0.018923743379700153, 0.010976836172447269, -0.017847220928846164,
> 0.0024067380689056783, -0.011912393656503872, -0.019985462687391875,
> 0.017318878212931876, 0.003592873590795409, -0.00332615776078915,
> -0.018222673013956525, -0.021591768336351125];
> [h, p] = lillietest(sampleVector)
> Warning: P is greater than the largest tabulated value, returning 0.5.
> > In lillietest at 166
> h =
> 0
> p =
> 0.5000
> This uses Lilliefors test for normality. The test returns that h=0, i.e. the
> null hypothesis that the data vector obeys Normal distribution.
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