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Activity Number: 72 - SPEED: Statistical Learning and Data Challenge Part 2
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 4:45 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323711
Title: LinCDE: Conditional Density Estimation via Lindsey's Method
Author(s): Zijun Gao* and Trevor Hastie
Companies: Stanford University and Stanford University
Keywords: Conditional Density Estimation; Gradient Boosting; Lindsey's Method

Conditional density estimation is a fundamental problem in statistics, with scientifi c and practical applications in biology, economics, finance and environmental studies, to name a few. In this work, 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 real data examples.

Authors who are presenting talks have a * after their name.

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