Software Open Access
haldensify: Highly Adaptive Lasso Conditional Density Estimation
Hejazi, Nima S;
Benkeser, David C;
van der Laan, Mark J
Conditional density estimation is a longstanding and challenging problem in statistical theory, and numerous proposals exist for optimally estimating such complex functions. Algorithms for nonparametric estimation of conditional densities based on a pooled hazard regression formulation and semiparametric estimation via conditional hazards modeling are implemented based on the highly adaptive lasso, a nonparametric regression function for efficient estimation with fast convergence under mild assumptions. The pooled hazards formulation implemented was first described by Díaz and van der Laan (2011) <https://doi.org/10.2202/1557-4679.1356>.
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haldensify-0.0.5.tar.gz
md5:e379e2e9bbcf57f8308b2990e825e514 |
195.4 kB | Download |
| All versions | This version | |
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| Views | 37 | 37 |
| Downloads | 2 | 2 |
| Data volume | 390.8 kB | 390.8 kB |
| Unique views | 26 | 26 |
| Unique downloads | 2 | 2 |
- Publication date:
- March 5, 2020
- DOI:
-
- Keyword(s):
- density estimation machine learning nonparametric regression semiparametric estimation
- License (for files):
- MIT License