Saved in:
| Main Authors: | Qin, Jing, Liu, Yukun, Li, Moming, Huang, Chiung-Yu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.16513 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
by: Fan, Jianqing, et al.
Published: (2023)
by: Fan, Jianqing, et al.
Published: (2023)
Neyman-Pearson multiclass classification under label noise via empirical likelihood
by: Zhang, Qiong, et al.
Published: (2026)
by: Zhang, Qiong, et al.
Published: (2026)
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
by: Tian, Ye, et al.
Published: (2021)
by: Tian, Ye, et al.
Published: (2021)
Beyond Neyman-Pearson: e-values enable hypothesis testing with a data-driven alpha
by: Grünwald, Peter
Published: (2022)
by: Grünwald, Peter
Published: (2022)
Integration of Individual Participant and Aggregate Data Under Dataset Shift: Summary Statistic Comparison and Scalable Computation
by: Huang, Ming-Yueh, et al.
Published: (2026)
by: Huang, Ming-Yueh, et al.
Published: (2026)
Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
by: Liu, Lingchong, et al.
Published: (2025)
by: Liu, Lingchong, et al.
Published: (2025)
Instability of inverse probability weighting methods and a remedy for non-ignorable missing data
by: Li, Pengfei, et al.
Published: (2025)
by: Li, Pengfei, et al.
Published: (2025)
Adaptive Neyman Allocation
by: Zhao, Jinglong
Published: (2023)
by: Zhao, Jinglong
Published: (2023)
Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
by: Li, Mengke, et al.
Published: (2025)
by: Li, Mengke, et al.
Published: (2025)
Retrospective score tests versus prospective score tests for genetic association with case-control data
by: Liu, Yukun, et al.
Published: (2025)
by: Liu, Yukun, et al.
Published: (2025)
Transportable inference using target population summary statistics under covariate shift
by: Sheng, Ying, et al.
Published: (2026)
by: Sheng, Ying, et al.
Published: (2026)
Maximum likelihood abundance estimation from capture-recapture data when covariates are missing at random
by: Liu, Yang, et al.
Published: (2025)
by: Liu, Yang, et al.
Published: (2025)
Conformal predictive intervals in survival analysis: a re-sampling approach
by: Qin, Jing, et al.
Published: (2024)
by: Qin, Jing, et al.
Published: (2024)
A Neyman-Orthogonalization Approach to the Incidental Parameter Problem
by: Bonhomme, Stéphane, et al.
Published: (2024)
by: Bonhomme, Stéphane, et al.
Published: (2024)
Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
by: Kato, Masahiro
Published: (2024)
by: Kato, Masahiro
Published: (2024)
Complementary strengths of the Neyman-Rubin and graphical causal frameworks
by: Gorbach, Tetiana, et al.
Published: (2025)
by: Gorbach, Tetiana, et al.
Published: (2025)
On the Equivalence between Neyman Orthogonality and Pathwise Differentiability
by: Chen, Yuxi, et al.
Published: (2026)
by: Chen, Yuxi, et al.
Published: (2026)
Bayesian inference for Neyman-Scott point processes with anisotropic clusters
by: Dvořák, Jiří, et al.
Published: (2025)
by: Dvořák, Jiří, et al.
Published: (2025)
Optimal Model Selection for Conformalized Robust Optimization
by: Bao, Yajie, et al.
Published: (2025)
by: Bao, Yajie, et al.
Published: (2025)
Semiparametric Learning from Open-Set Label Shift Data
by: Liu, Siyan, et al.
Published: (2025)
by: Liu, Siyan, et al.
Published: (2025)
Pearson's correlation under the scope: Assessment of the efficiency of Pearson's correlation to select predictor variables for linear models
by: Attallah, Mustafa
Published: (2024)
by: Attallah, Mustafa
Published: (2024)
Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization
by: Bai, Tian, et al.
Published: (2024)
by: Bai, Tian, et al.
Published: (2024)
Semi-parametric Bayesian inference under Neyman orthogonality
by: Sabbagh, Magid, et al.
Published: (2026)
by: Sabbagh, Magid, et al.
Published: (2026)
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
by: Li, Michael Lingzhi, et al.
Published: (2024)
by: Li, Michael Lingzhi, et al.
Published: (2024)
Critical issues with the Pearson's chi-square test
by: Gurvich, Vladimir, et al.
Published: (2025)
by: Gurvich, Vladimir, et al.
Published: (2025)
Stronger Neyman Regret Guarantees for Adaptive Experimental Design
by: Noarov, Georgy, et al.
Published: (2025)
by: Noarov, Georgy, et al.
Published: (2025)
Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators
by: Chen, Fangyi, et al.
Published: (2025)
by: Chen, Fangyi, et al.
Published: (2025)
Diversifying Conformal Selections
by: Nair, Yash, et al.
Published: (2025)
by: Nair, Yash, et al.
Published: (2025)
Causal generalized linear models via Pearson risk invariance
by: Polinelli, Alice, et al.
Published: (2024)
by: Polinelli, Alice, et al.
Published: (2024)
Pearson Chi-squared Conditional Randomization Test
by: Javanmard, Adel, et al.
Published: (2021)
by: Javanmard, Adel, et al.
Published: (2021)
Neyman Jackknife: Design-Based Variance Estimation for Causal Inference under Interference
by: Park, Bryan, et al.
Published: (2026)
by: Park, Bryan, et al.
Published: (2026)
Multivariate Conformal Selection
by: Bai, Tian, et al.
Published: (2025)
by: Bai, Tian, et al.
Published: (2025)
Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing
by: Zhu, Ke, et al.
Published: (2024)
by: Zhu, Ke, et al.
Published: (2024)
Classification-Powered Conformal Inference for Zero-inflated Outcomes
by: Li, Zhirui, et al.
Published: (2026)
by: Li, Zhirui, et al.
Published: (2026)
int3ract: Johnson-Neyman Technique and its Three-Way Extension for Frequentist and Bayesian Models in R
by: Krause, Robert W.
Published: (2026)
by: Krause, Robert W.
Published: (2026)
Penalized empirical likelihood estimation and EM algorithms for closed-population capture-recapture models
by: Liu, Yang, et al.
Published: (2022)
by: Liu, Yang, et al.
Published: (2022)
Selection and Aggregation of Conformal Prediction Sets
by: Yang, Yachong, et al.
Published: (2021)
by: Yang, Yachong, et al.
Published: (2021)
Distributed Conditional Feature Screening via Pearson Partial Correlation with FDR Control
by: Pang, Naiwen, et al.
Published: (2024)
by: Pang, Naiwen, et al.
Published: (2024)
Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata
by: Wesołowski, Jacek, et al.
Published: (2023)
by: Wesołowski, Jacek, et al.
Published: (2023)
Selection from Hierarchical Data with Conformal e-values
by: Lee, Yonghoon, et al.
Published: (2025)
by: Lee, Yonghoon, et al.
Published: (2025)
Similar Items
-
Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
by: Fan, Jianqing, et al.
Published: (2023) -
Neyman-Pearson multiclass classification under label noise via empirical likelihood
by: Zhang, Qiong, et al.
Published: (2026) -
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
by: Tian, Ye, et al.
Published: (2021) -
Beyond Neyman-Pearson: e-values enable hypothesis testing with a data-driven alpha
by: Grünwald, Peter
Published: (2022) -
Integration of Individual Participant and Aggregate Data Under Dataset Shift: Summary Statistic Comparison and Scalable Computation
by: Huang, Ming-Yueh, et al.
Published: (2026)