  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.
