Journal
JOURNAL OF MACHINE LEARNING RESEARCH
Volume 23, Issue -, Pages 1-55Publisher
MICROTOME PUBL
Keywords
Conditional Density Estimation; Gradient Boosting; Lindsey's Method
Funding
- National Science Foundation [DMS-2013736, IIS 1837931]
- National Institutes of Health [5R01 EB 001988-21]
Ask authors/readers for more resources
In this paper, we propose a conditional density estimator (LinCDE) based on gradient boosting and Lindsey's method. LinCDE allows flexible modeling of density family and captures distributional characteristics. It produces smooth and non-negative density estimates.
Conditional density estimation is a fundamental problem in statistics, with scientific and practical applications in biology, economics, finance and environmental studies, to name a few. In this paper, we propose a conditional density estimator based on gradient boosting and Lindsey's method (LinCDE). LinCDE admits flexible modeling of the density family and can capture distributional characteristics like modality and shape. In particular, when suitably parametrized, LinCDE will produce smooth and non-negative density estimates. Furthermore, like boosted regression trees, LinCDE does automatic feature selection. We demonstrate LinCDE's efficacy through extensive simulations and three real data examples.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available