mlsanityData to 14 Oct 2026

LinCDE: Conditional Density Estimation via Lindsey's Method

arXiv 2107.12713 ↗First posted 27 Jul 2021stat.MEcited by 13

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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.

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

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Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 5; 2026: 3.

13 papers.

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  1. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  2. 2024Applied Causal Inference Powered by ML and AIcited by 30
  3. 2024Neural Conditional Probability for Uncertainty Quantificationcited by 14
  4. 2024A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Modelscited by 4
  5. 2024Conditional Density Estimation with Histogram Treescited by 4
  6. 2025Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localizationcited by 1
  7. 2025CINDES: Classification induced neural density estimator and simulatorcited by 1
  8. 2025Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networkscited by 1
  9. 2025Transforming Conditional Density Estimation Into a Single Nonparametric Regression Taskcited by 1
  10. 2025Wasserstein Regression as a Variational Approximation of Probabilistic Trajectories through the Bernstein Basiscited by 0
  11. 2026A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernelscited by 0
  12. 2026LOCUS-DT: Localization via Observation-Conditioned Uncertainty Scoring with Digital Twinscited by 0
1 more
  1. 2026XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matchingcited by 0