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ANM, lack of Gamma in "Gamma HSIC" #58
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For future reference: this test essentially compares the test statistics |
Hi, Feel free to make a pull request ; it might take some time before I could look into it. |
Hi,
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Hi, |
Hi,
This might simply be a conceptual problem, or a lack of knowledge on my part.
Usually, using HSIC to compare two ANM candidates can be done by comparing the statistics directly, or by computing the related p-value.
However, to compute a p-value one needs to have some notion of the HSIC distribution under the null.
The classic paper from Gretton et al. proposes a Gamma Approximation by giving specific plug-in values for the two Gamma parameters in terms of the expectation and variance of the HSIC;
If I had to compute the p-value myself, I would use the above approximation for the gamma distribution, and then use the gamma CDF parametrized by the above values.
I am aware there might be other ways to do such a thing, however your snipper in the anm method does not seem to compute p-values, but only test statistics.
While this might be wrong, the variable names as well as the description of the method suggests this.
Am I wrong? Right? If either, how so?
Thanks for any additionnal information on this topic,
I would ideally like to design a test which detects whenever a model satisfies an ANM with low Type I and II error.
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