Optimal subgroup selection
From MaRDI portal
Publication:6183867
DOI10.1214/23-aos2328arXiv2109.01077OpenAlexW3197496818MaRDI QIDQ6183867
Richard J. Samworth, Timothy I. Cannings, Henry W. J. Reeve
Publication date: 4 January 2024
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.01077
Cites Work
- Unnamed Item
- Bandwidth selection for kernel density estimators of multivariate level sets and highest density regions
- Confidence regions for level sets
- Responder identification in clinical trials with censored data
- Asymptotics and optimal bandwidth selection for highest density region estimation
- Fast learning rates for plug-in classifiers
- A constrained risk inequality with applications to nonparametric functional estimation
- On nonparametric estimation of density level sets
- Adaptive confidence interval for pointwise curve estimation.
- Measuring mass concentrations and estimating density contour clusters -- An excess mass approach
- Honest adaptive confidence bands and self-similar functions
- Adaptive transfer learning
- Asymptotics and optimal bandwidth for nonparametric estimation of density level sets
- Exact asymptotic limit for kernel estimation of regression level sets
- Confidence bands in density estimation
- Asymptotic normality of plug-in level set estimates
- Nonparametric confidence regions for level sets: statistical properties and geometry
- Nonparametric estimation of regression level sets using kernel plug-in estimator
- A Neyman–Pearson Approach to Statistical Learning
- Nonparametric Estimation of Regression Level Sets
- Regression Level Set Estimation Via Cost-Sensitive Classification
- High-Dimensional Probability
- Design and Analysis of Subgroups with Biopharmaceutical Applications
- Smoothness-Adaptive Contextual Bandits
- Understanding Machine Learning
- Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification
This page was built for publication: Optimal subgroup selection