2010/11/7 Phil Steitz <phil.steitz@gmail.com>:
> 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 Kolmogorov’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
Thanks. It looks quite thorough, indeed. Was it the Java
implementation you didn't get a consent to release under ASL?
>
>
>>>>>>
>>>>>> 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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