Yes, I think we would be interested in confidence intervals, but I think the algorithm should be very well standard/cited, even if it's not the best/most modern.

Yes definitely! We did not invented the method I am referring to, it a long-known approach (first proposed by Thompson in 1936 [1], extended later and commonly found in textbooks, eg [2,3]). This method is very simple, quite powerful, yet it has been largely overlooked in many scientific fields. I found no available implementation to facilitate its use (at least not in Python, there may be something in R, I have not looked).

[1] https://www.jstor.org/stable/2957563
[2] doi.org/10.1002/0471722162.ch7
[3] https://perfeval.epfl.ch/

@WarrenWeckesser and I had planned to work on confidence intervals for the test statistics returned by our statistical tests.

That is also definitely interesting, although I am not myself an expert in that area. I am glad to see that the complete list contains some non-parametric tests :-)

Cheers,
--
Romain

On Mon, Jun 8, 2020 at 2:11 AM Romain Jacob <jacobr@ethz.ch> wrote:

Hello everyone,

I have been working for some time on the implementation of non-parametric methods to compute confidence intervals for percentiles. There are some very interesting results in the literature (see e.g. a nice pitch in [1]) which I think it would be great to add to SciPy to make them more readily available. It also seems to be rather in line with "recent" discussions of the roadmap for scipy.stats [2].

I would be interested in contributing this. What do you think?

Cheers,
--
Romain

[1] https://ieeexplore.ieee.org/document/6841797
[2] https://github.com/scipy/scipy/issues/10577

--

Romain Jacob

Postdoctoral Researcher

ETH Zurich - Computer Engineering and Networks Laboratory

www.romainjacob.net
@RJacobPartner

Gloriastrasse 35, ETZ G75
8092 Zurich
+41 7 68 16 88 22

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--

Matt Haberland

Assistant Professor

BioResource and Agricultural Engineering

08A-3K, Cal Poly

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