Support for obtaining p-values for significance tests from bootstrap classes
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
Research direction
Start by reading the conf_int method on IIDBootstrap and StationaryBootstrap and comparing how their confidence intervals are calculated. The issue proposes inverting that calculation to obtain a p-value, but leaves technical caveats and trade-offs open. Done means defining the method’s behavior and adding tests that verify p-values for the supported bootstrap classes.
Written by the indexing model from the issue text.
Description
Bootstrap classes (IIDBootstrap, StationaryBootstrap etc) support the conf_int method which allows to build confidence intervals. This can also be used (subject no doubt to some technical caveats/trade-offs depending on the type of CI used) to perform significance testing by checking whether the null value falls into the interval or not. But this requires the significance level alpha to be fixed. It would be nice if there was also a method to obtain a p-value. This would require inverting the confidence interval calculation, but looks doable.
- Dominant language
- Python
- Stars
- 1.6k
- Forks
- 298
- Avg merge
- 14h 46m
- Merged PRs (30d)
- 106
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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