josemiotto / pylevy

Levy distributions for Python
GNU General Public License v3.0
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Bibtex #17

Open JulienSiems opened 2 years ago

JulienSiems commented 2 years ago

I am using your code and it would be good to able to cite your repo! Could you post how you'd like it to be cited?

Thanks for your great work!

josemiotto commented 2 years ago

Hi Julien!

You can cite this zenodo link, which has a doi, or the paper associated with it (there is a link on the zenodo page)

https://zenodo.org/record/53787#.Yb8KzEYo-Nx

I realize that it has an old version, I.e. not synced with github. When I come back from vacations in a week I will update it, if you want to wait.

Best, and thanks, José

On Thu, 16 Dec 2021, 17:57 Julien, @.***> wrote:

I am using your code and it would be good to able to cite your repo! Could you post how you'd like it to be cited?

Thanks for your great work!

— Reply to this email directly, view it on GitHub https://github.com/josemiotto/pylevy/issues/17, or unsubscribe https://github.com/notifications/unsubscribe-auth/AACCTUUHH2ARE6E72KHRAMDURILBBANCNFSM5KGZ2ASQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

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JulienSiems commented 2 years ago

Hi José,

Great, no rush! Let me know once you have updated it. I am pretty surprised how well fitting a stable distribution via your tabulated values works. I compared against a more classical estimator for the tail index for samples of a stable distribution with different tail indices and skewness and your estimator was an order of magnitude more accurate in terms of mean absolute error.

I did notice some outliers in the estimation, though. It may help to implement retries for the optimization in scipy or use evolutionary algorithm, like differential evolution, rather than gradient-based optimization?

Best, Julien

josemiotto commented 2 years ago

Hi,

It is surprising indeed haha. I don't know what estimators you are using but the classic problem there is to choose a boundary for the tail.

Can I ask you for what are you using it?

Best, José

On Sun, 19 Dec 2021, 12:09 Julien, @.***> wrote:

Hi José,

Great, no rush! Let me know once you have updated it. I am pretty surprised how well fitting a stable distribution via your tabulated values works. I compared against a more classical estimator for the tail index for samples of a stable distribution with different tail indices and skewness and your estimator was an order of magnitude more accurate in terms of mean absolute error.

Best, Julien

— Reply to this email directly, view it on GitHub https://github.com/josemiotto/pylevy/issues/17#issuecomment-997372748, or unsubscribe https://github.com/notifications/unsubscribe-auth/AACCTUSTKGC5XMJN5QQCKCLURW4QBANCNFSM5KGZ2ASQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

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JulienSiems commented 2 years ago

Hi José,

but the classic problem there is to choose a boundary for the tail.

Do you mean to find the tail index by fitting an exponential decay to the tails?

I am using this one: https://link.springer.com/article/10.1007/s00184-014-0515-7 (Corollary 2.4) It's for example used quite extensively in: https://arxiv.org/abs/1901.06053 Below, is the plot I meant for the absolute alpha estimation error for different stable samples.

image

Best,

Julien