Switching to the right list...

>>>
>>> What we need there is a good algorithm for approximating the KS
>>> distribution. I have been corresponding with the author of a very good
>>> one
>>> with a Java implementation but have thus far failed in getting consent to
>>> release under ASL. So at this point, I am looking for an alternative
>>> good
>>> algorithm to implement. All suggestions / unencumbered patches welcome!
>>>
>>> See comments on the MATH431 for other questions.
>>>
>> Just to be sure of what you mean:
>> Do you want to have a twosample KolmogorovSmirnov test for equality
>> of distributions in addition to the MannWhitney? Or do you need the
>> KolmogorovSmirnov distribution (as stated for example at
>>
>> http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Kolmogorov_distribution
>> ) in regards to the MATH428? Sorry, but I'm at bit confused :).
>
> The goal is to implement the KS test for equality of distributions (or
> homogeneity against a reference distribution). To do that we need at least
> critical values of the Kolmogorov distribution. The natural way for us to
> do that would be to implement the full distribution which would be nice to
> have in the distributions package.
>
> Phil
Have you read "Evaluating Kolmogorovâ€™s Distribution" by Marsaglia et
al. available on http://www.jstatsoft.org/v08/i18/paper ? And do you
think their approach would be the way to go?
I am not sure it is best. See the comments here:
http://www.iro.umontreal.ca/~lecuyer/myftp/papers/ksdist.pdf
Phil
>>>>>
>>>>> Interesting approach for the exact algorithm for Wilcoxon. If we stay
>>>>> with this, we should ack the original author of the algorithm in the
>>>>> javadoc. Looks OK to use.
>>>>
>>>> Agree  both on the approach and legal part! Does the author need to
>>>> sign anything but write a mail?
>>>>>
>>>>> Regarding the difference from R, what I usually do in this case is
>>>>> look
>>>>> at the R sources to try to explain the difference. Most likely in this
>>>>> case, what is going on is they are using a different estimation
>>>>> algorithm
>>>>> for small n or treating ties differently. The ranking options that we
>>>>> use
>>>>> were largely adapted from R, so if that is the problem, it should be
>>>>> easy to
>>>>> test. We need to convince ourselves that ours is better or at least
a
>>>>> legitimate alternative. I will take a close look this evening, but it
>>>>> looks
>>>>> like the algorithm you are using should be exact. If we can't
>>>>> reconcile the
>>>>> difference with R, it would be good to find a way to validate correct
>>>>> functioning of the algorithm by manufacturing reference data with known
>>>>> p.
>>>>
>>>> I'll try to investigate the difference, hopefully tomorrow, so that
>>>> formal tests can be written and included.
>>>>>
>>>>>> New tests: Wilcoxon signedrank test and MannWhitney U
>>>>>> 
>>>>>>
>>>>>> Key: MATH431
>>>>>> URL: https://issues.apache.org/jira/browse/MATH431
>>>>>> Project: Commons Math
>>>>>> Issue Type: New Feature
>>>>>> Reporter: Mikkel Meyer Andersen
>>>>>> Assignee: Mikkel Meyer Andersen
>>>>>> Priority: Minor
>>>>>> Attachments: MannWhitneyUTest.java, MannWhitneyUTestImpl.java,
>>>>>> WilcoxonSignedRankTest.java, WilcoxonSignedRankTestImpl.java
>>>>>>
>>>>>> Original Estimate: 4h
>>>>>> Remaining Estimate: 4h
>>>>>>
>>>>>> Wilcoxon signedrank test and MannWhitney U are commonly used
>>>>>> nonparametric statistical hypothesis tests (e.g. instead of various
>>>>>> ttests
>>>>>> when normality is not present).
>>>>>
>>>>> 
>>>>> This message is automatically generated by JIRA.
>>>>> 
>>>>> You can reply to this email to add a comment to the issue online.
>>>>>
>>>>>
>>>
>>>
>
>

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