A nonparametric test based on regression error (FIT)
Open
@MatthewZhao26 is already working on this.
Since Feb 9, 2022.
enhancement
ndd
- Dominant language
- Python
- Stars
- 252
- Forks
- 96
- PR merge metrics
- No merged PRs in 30d
Description
A nonparametric test based on regression error (FIT) [paper] [python code]
- A bit more fringe than KCI/KCIP but provides good simulation comparisons between all three methods plus more.
- Uses a nonparametric regression (in their case, a decision tree) to examine the change in predictive power based on including versus excluding some variables Z.
- Uses the mean squared error as a test statistic and an analytic Gaussian/T-test approach to compute a pvalue
- Seemingly efficient for large samples sizes as compared to other kernel based approaches.
- Interesting connections in that trees/forests are adaptive kernel methods and extensions to forests/honesty/leaf permutations.
Contributor guide
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.
Assessment
This issue has not been assessed yet.