Nonparametric inference under shape constraints: past, present and future
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Abstract (arXiv)
We survey the field of nonparametric inference under shape constraints, providing a historical overview and a perspective on its current state. An outlook and some open problems offer thoughts on future directions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 64 references that are in the map. First posted on arXiv 30 Sep 2025.
Builds on
13 of 64 references.
References · 13
- 2021How to Train Your Energy-Based Modelscited by 274
- 2018Time-uniform, nonparametric, nonasymptotic confidence sequencescited by 229
- 2020Isotonic regression with unknown permutations: Statistics, computation, and adaptationcited by 13
- 2019Optimality of Maximum Likelihood for Log-Concave Density Estimation and Bounded Convex Regressioncited by 10
- 2024Optimal convex M-estimation via score matchingcited by 9
- 2020Convex Regression in Multidimensions: Suboptimality of Least Squares Estimatorscited by 7
- 2021A new computational framework for log-concave density estimationcited by 4
- 2019High-dimensional nonparametric density estimation via symmetry and shape constraintscited by 3
- 2024Log-concave Density Estimation with Independent Componentscited by 3
- 2021Nonparametric, tuning-free estimation of S-shaped functionscited by 2
- 2021Optimal subgroup selectioncited by 2
- 2023Isotonic subgroup selectioncited by 2
1 more
- 2025Testing conditional independence under isotonicitycited by 1
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Citing papers per year: 2026: 3.
3 papers.