期刊
JOURNAL OF MACHINE LEARNING RESEARCH
卷 23, 期 -, 页码 1-55出版社
MICROTOME PUBL
关键词
Conditional Density Estimation; Gradient Boosting; Lindsey's Method
资金
- National Science Foundation [DMS-2013736, IIS 1837931]
- National Institutes of Health [5R01 EB 001988-21]
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.
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