On Mon, Aug 17, 2020 at 9:44 AM Romain Jacob <jacobr@ethz.ch> wrote:

Hello everyone,

I've submitted the PR adding support for non-parametric confidence intervals for quantiles (https://github.com/scipy/scipy/pull/12680). There has been quite some comments made already, which I fixed appropriately I believe

Will be happy to get some more feedback or see the PR merged :-)

Note: the last commit has a CI failing apparently due to a file change in `scipy/sparse/linalg/` which is completely unrelated. I'm not sure how to go about this... ?

If it's clearly unrelated, you can just ignore it. Or add a comment "the only CI failure is in sparse.linalg and unrelated to this PR". Then the reviewer can just go ahead and merge if everything else looks good - CI doesn't have to be green.

Cheers,
Ralf

Cheers,
--
Romain

On 15/06/2020 08:27, Romain Jacob wrote:
On 13/06/2020 20:54, josef.pktd@gmail.com wrote:
On Fri, Jun 12, 2020 at 11:29 AM <josef.pktd@gmail.com> wrote:
On Fri, Jun 12, 2020 at 1:58 AM Romain Jacob <jacobr@ethz.ch> wrote:
On 11/06/2020 20:54, Warren Weckesser wrote:
On 6/11/20, josef.pktd@gmail.com <josef.pktd@gmail.com> wrote:

I think it would make a good and useful addition and fit into scipy.stats.
There are no pure confint functions yet, AFAIR.

I agree with Josef and Matt, this looks like it would be a nice
addition to SciPy.  At the moment, I'm not sure what the API should
look like.  Romain, is the work that you've already done available
online somewhere?

Warren

Yes, I have some functional implementation available here: https://github.com/TriScale-Anon/triscale/blob/master/helpers.py#L397


An implementation detail: 
binom has cdf and ppf functions
My guess, not verified, is that we can just use binom.interval

(at least I used those for similar cases)

I found my version again

I guess that's the same for two sided confint as the references. 
It doesn't have interpolation if that could be applied in this case. 

I don't entirely follow what you mean here: that the building of the probabilities in these two lines( https://github.com/TriScale-Anon/triscale/blob/master/helpers.py#L438 and L439) can be built directly form binom without np.cumsum? That definitely correct (I actually have code also doing that somewhere).

I did not know about the `interval` method. That's sound interesting indeed, but it's not 100% clear to me how the uniqueness problem is handled. I looked for the implementation of the method but couldn't find it in `binom`... I'm looking in the wrong place?

Cheers,
--
Romain


This will eventually end up in statsmodels, but I don't know yet where. That's not a reason not to add it to scipy.stats.

Josef


Josef
 

There is quite some work to be done on formatting and documentation to comply with the SciPy standards, but functionally it's already there (and as you'll see, the method is quite simple).

Cheers,
--
Romain

I recently wrote a function for the confidence interval for the median,
mainly because I ran into the formulas that were easy to code.
related open issue: how do we get confidence intervals for QQ-plot.

aside: I don't like "percent", I prefer quantiles in [0, 1]. See discussion
a while ago in numpy.

Josef


On Thu, Jun 11, 2020 at 1:01 PM Matt Haberland <mhaberla@calpoly.edu>
wrote:


OK, we should let our statistics experts weigh in on this. (I'm not
actually one of them.)

On Wed, Jun 10, 2020 at 10:46 PM Romain Jacob <jacobr@ethz.ch> wrote:


I think a dedicated function makes more sense. This function takes as
input an array, a percentile and a confidence level, and returns the
corresponding one-sided confidence intervals.

I quickly looked at the list of existing functions in scipy.stats but
did
not see any function in "summary statistics" that does similar things. So
I
would go for a new function.
On 10/06/2020 20:38, Matt Haberland wrote:

Where do you envision this living in SciPy? In its own function, or
added
functionality to other functions e.g. scipy.stats.percentileofscore
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.percentileofscore.html#scipy.stats.percentileofscore>
?

On Tue, Jun 9, 2020 at 11:12 PM Romain Jacob <jacobr@ethz.ch> wrote:


On 09/06/2020 20:18, Matt Haberland wrote:

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
<https://docs.scipy.org/doc/scipy/reference/stats.html#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
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Matt Haberland
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BioResource and Agricultural Engineering
08A-3K, Cal Poly

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Romain Jacob
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ETH Zurich - Computer Engineering and Networks Laboratory
www.romainjacob.net
@RJacobPartner <https://twitter.com/RJacobPartner>
Gloriastrasse 35, ETZ G75
8092 Zurich
+41 7 68 16 88 22
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BioResource and Agricultural Engineering
08A-3K, Cal Poly

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8092 Zurich
+41 7 68 16 88 22
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Romain Jacob
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8092 Zurich
+41 7 68 16 88 22 

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