HiDimStat working only with Python 3, ideally Python 3.6+. For installation, run the following from terminal
git clone https://github.com/ja-che/hidimstat.git
cd hidimstat
pip install -e .joblib
numpy
scipy
scikit-learn
To run examples it is neccessary to install matplotlib, and to run tests it
is also needed to install pytest.
All the documentation of HiDimStat is available at https://ja-che.github.io/hidimstat/.
As of now in the examples folder there is a Python script to reproduce Figure
1 in Nguyen et al. 2020 (see References below). Warning: this script
should take quite a long time to run.
# Run this command in terminal
python plot_fig_1_nguyen_et_al.py
Ensemble of Clustered desparsified Lasso (ECDL):
- Chevalier, J. A., Salmon, J., & Thirion, B. (2018). Statistical inference with ensemble of clustered desparsified lasso. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 638-646). Springer, Cham.
Aggregation of multiple Knockoffs (AKO):
- Nguyen T.-B., Chevalier J.-A., Thirion B., & Arlot S. (2020). Aggregation of Multiple Knockoffs. In Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119.
If you use our packages, we would appreciate citations to the aforementioned papers.
For de-sparsified(or de-biased) Lasso:
-
Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. The Journal of Machine Learning Research, 15(1), 2869-2909.
-
Zhang, C. H., & Zhang, S. S. (2014). Confidence intervals for low dimensional parameters in high dimensional linear models. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 217-242.
For Knockoffs Inference:
-
Barber, R. F; Candès, E. J. (2015). Controlling the false discovery rate via knockoffs. Annals of Statistics. 43 , no. 5, 2055--2085. doi:10.1214/15-AOS1337. https://projecteuclid.org/euclid.aos/1438606853
-
Candès, E., Fan, Y., Janson, L., & Lv, J. (2018). Panning for gold: Model-X knockoffs for high dimensional controlled variable selection. Journal of the Royal Statistical Society Series B, 80(3), 551-577.