Standard error on best fit parameters of stretched exponential about powerlaw

[jeffalstott]

Glad it's useful to you! They are not implemented. We'll definitely take it on board if you implement it (accurately >_> ).

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On Wed, Jan 31, 2018 at 6:08 PM, Khev ***@***.***> wrote: Hi there, Thanks for such an amazing package. It's made my life hell of a lot easier! One question: I can't figure out how to get the standard errors on the best fit parameters for the non-powerlaw distributions (i.e. lognormal, stretched expontial, etc). Am I being stupid? Or is this not provided. If the latter, it would be great if it could be implemented! I would happily provide help implementing if you need / want it. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#49>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AA6_rztzI7tQM4T4YsdpC7hhaTMPCtnaks5tQPJQgaJpZM4R0y8f> .

[Khev]

Challenge accepted! I'm reading through Aaron Clausets and Mark Newman's nice paper on MLE estiamtion -- https://arxiv.org/pdf/0706.1062.pdf --. They have an analytic formula in the appendix for the standard errors for a given distribution. I'll work out the results for the streched exponential, etc and do some tests and let you know the results.

One thought: I could use bayeisan inference to estimate the error is the parameters also. Python has a nice MCMC module -- called pyMc -- which should do the trick.