# LinCDE: Conditional Density Estimation via Lindsey's Method

arXiv: https://arxiv.org/abs/2107.12713 · HTML: https://mlsanity.com/paper/2107.12713/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2021.

## Built on by (most cited first, 13 of 13)

- [Adversarial random forests for density estimation and generative modeling](https://mlsanity.com/paper/2205.09435/) (2022)
- [Applied Causal Inference Powered by ML and AI](https://mlsanity.com/paper/2403.02467/) (2024)
- [Neural Conditional Probability for Uncertainty Quantification](https://mlsanity.com/paper/2407.01171/) (2024)
- [A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models](https://mlsanity.com/paper/2410.02025/) (2024)
- [Conditional Density Estimation with Histogram Trees](https://mlsanity.com/paper/2410.11449/) (2024)
- [Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization](https://mlsanity.com/paper/2509.25719/) (2025)
- [CINDES: Classification induced neural density estimator and simulator](https://mlsanity.com/paper/2510.00367/) (2025)
- [Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks](https://mlsanity.com/paper/2511.14455/) (2025)
- [Transforming Conditional Density Estimation Into a Single Nonparametric Regression Task](https://mlsanity.com/paper/2511.18530/) (2025)
- [Wasserstein Regression as a Variational Approximation of Probabilistic Trajectories through the Bernstein Basis](https://mlsanity.com/paper/2510.26607/) (2025)
- [A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels](https://mlsanity.com/paper/2606.02232/) (2026)
- [LOCUS-DT: Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins](https://mlsanity.com/paper/2608.00406/) (2026)
- [XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching](https://mlsanity.com/paper/2609.34939/) (2026)

## Abstract (arXiv)

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.