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R: I: Re: Kolmogorov Smirnov in two or more dimensions is in Mathematica 8.0.4

  • To: mathgroup at smc.vnet.net
  • Subject: [mg124991] R: I: Re: Kolmogorov Smirnov in two or more dimensions is in Mathematica 8.0.4
  • From: maria giovanna dainotti <mariagiovannadainotti at yahoo.it>
  • Date: Thu, 16 Feb 2012 03:28:49 -0500 (EST)
  • Delivered-to: l-mathgroup@mail-archive0.wolfram.com

Dear Andy Ross,
I have just got information of a new release Mathematica 8.0.4.
Do you know if the bug of Kolmogorv Smirnov in two dimension is fixed in that version?
Unfortunately, for my purpose it is better to use a statistical tools that make comparison directly with the data not assuming any stastistic.

I would be very grateful if you could let me know
Best regards,
Maria


--- Mer 8/2/12, maria giovanna dainotti <mariagiovannadainotti at yahoo.it> has critto:

Da: maria giovanna dainotti <mariagiovannadainotti at yahoo.it>
Oggetto: I: Re: Kolmogorov Smirnov in two or more dimensions
A:
Data: Mercoled=EC 8 febbraio 2012, 20:58



--- Mer 8/2/12, Andy Ross <andyr at wolfram.com> ha scritto:

Da: Andy Ross <andyr at wolfram.com>
Oggetto: Re: Kolmogorov Smirnov in two or more dimensions
A: "maria giovanna dainotti" <mariagiovannadainotti at yahoo.it>
Cc: mathgroup at smc.vnet.net
Data: Mercoled=EC 8 febbraio 2012, 17:09

This is a bug in KolmogorovSmirnovTest that will be fixed in later versions of Mathematica. For the time being you can use CramerVonMisesTest or DistributionFitTest.

Note that DistributionFitTest uses a little known test referred to as Szekely-Energy. A description can be found in...

Szekely, G. J. and Rizzo, M. L. (2004) Testing for Equal Distributions in High Dimension, InterStat,
November (5)

P-values for this test are computed via Monte-Carlo simulation at the moment so it tends to be slow.

The good thing about this test is that it doesn't just look at things marginally.  All of the other tests currently available perform tests on the marginal data and then aggregate the statistics. Thus any differences in covariance structure will be missed.

The take home message is that if things don't fit marginally then they don't fit jointly but if they do seem to fit well marginally you still need to dig deeper to determine whether the joint distributions are equivalent.

Andy Ross
Wolfram Research

On 2/8/2012 4:32 AM, maria giovanna dainotti wrote:
> Dear Mathematica group,
> I am doing an analysis of the Kolmogorov with two data sets of 2 dimensions each.
> When I apply the
 KolmogorovSmirnovTest[Data0,Data0] I should get 1. I did just a trial and I got 0.
> I am copying the datafile example for clarity.
> {{0.97304, 14.1829}, {0.98663, 14.1295}, {0.98284, 14.3172}, {0.81423,
>     14.3466}, {0.97303, 14.5966}, {0.87122, 14.8435}, {0.90252,
>    14.9036}, {0.81887, 15.1177}, {1.07722, 14.6849}, {0.86684,
>    15.6456}, {0.86664, 14.7034}, {0.78728, 15.0898}, {1.10336,
>    14.4085}, {1.12014, 14.7281}, {0.95923, 14.4988}, {0.89942,
>    15.3173}, {0.83841, 14.8422}, {0.99105, 14.8813}, {1.111,
>    14.5964}, {0.93255, 15.5019}, {1.03142, 14.8009}, {1.00661,
>    15.0827}, {0.93255, 15.3064}, {1.10023, 14.7189}, {0.8797,
>    15.1038}, {1.0013, 14.6755}, {0.87673, 15.0952}, {0.84131,
>    15.7345}, {1.06392, 15.3528}, {1.00138, 14.9835}, {0.77803,
>    15.4637}, {0.76795, 15.3611}, {0.98328, 15.1047}, {0.89193,
>    14.8321}, {0.72882, 15.909}, {0.77123, 15.7902}, {0.86218,
>    15.6637}, {0.84381, 15.536}, {0.99263, 15.8903}, {1.0805,
>    15.1453}, {0.85316, 15.7793}, {0.85186, 15.9119}, {1.10898,
>    15.7583}, {1.03365, 15.7393}, {0.84783, 16.1911}, {1.10979,
>    16.0031}, {1.05238, 16.015}, {0.90259, 16.4864}, {0.84963,
>    15.9818}, {1.09221, 16.0088}, {1.0443, 15.8326}, {0.8945,
>    16.1927}, {0.83015, 16.2776}, {0.7551, 16.5538}, {1.05947,
>    15.9138}, {1.06189, 15.6061}, {1.05889, 16.0743}, {0.85216,
>    16.1568}, {0.72597, 16.7657}, {0.93638, 16.3583}, {0.81968,
>    16.2711}, {0.95022, 16.3549}, {1.04536, 16.18}, {1.0786,
>    16.0967}, {0.9385, 15.8504}, {0.95024, 15.9338}, {0.76753,
>    17.1461}, {1.18224, 16.0437}, {0.96447, 16.4908}, {0.98235,
>    16.0892}, {1.06151, 16.699}, {0.79052, 16.5207}, {1.15863,
>    16.3223}, {1.00795, 16.2444}, {1.07284, 16.6536}, {1.04796,
>    16.865}, {0.84226, 16.7247}, {1.04712, 16.2673}}
>
> Or maybe should I use some other conditions?
> Or if it doesn't work is there an already built package that I can use?
>
> Thanks a lot for your help
>
> Best regards,
> Maria


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