:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Grünwald, Peter
Format:	Preprint
Published:	2022
Subjects:	Methodology
Online Access:	https://arxiv.org/abs/2205.00901
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On admissibility in post-hoc hypothesis testing
by: Chugg, Ben, et al.
Published: (2025)

Revamping Conformal Selection With Optimal Power: A Neyman--Pearson Perspective
by: Qin, Jing, et al.
Published: (2025)

Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
by: Fan, Jianqing, et al.
Published: (2023)

Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
by: Tian, Ye, et al.
Published: (2021)

Neyman-Pearson multiclass classification under label noise via empirical likelihood
by: Zhang, Qiong, et al.
Published: (2026)

Testing maximum entropy models with e-values
by: Giuffrida, Francesca, et al.
Published: (2025)

Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
by: Liu, Lingchong, et al.
Published: (2025)

E-values as statistical evidence: A comparison to Bayes factors, likelihoods, and p-values
by: Chugg, Ben, et al.
Published: (2026)

Critical issues with the Pearson's chi-square test
by: Gurvich, Vladimir, et al.
Published: (2025)

Adaptive Neyman Allocation
by: Zhao, Jinglong
Published: (2023)

E-Values for Exponential Families: the General Case
by: Hao, Yunda, et al.
Published: (2024)

E-values for k-Sample Tests With Exponential Families
by: Hao, Yunda, et al.
Published: (2023)

ALL-IN meta-analysis: breathing life into living systematic reviews and prospective meta-analyses
by: ter Schure, Judith, et al.
Published: (2021)

Goodness of fit by Neyman-Pearson testing
by: Grosso, Gaia, et al.
Published: (2023)

The general linear hypothesis testing problem for multivariate functional data with applications
by: Zhu, Tianming
Published: (2023)

Complementary strengths of the Neyman-Rubin and graphical causal frameworks
by: Gorbach, Tetiana, et al.
Published: (2025)

On the Equivalence between Neyman Orthogonality and Pathwise Differentiability
by: Chen, Yuxi, et al.
Published: (2026)

A Neyman-Orthogonalization Approach to the Incidental Parameter Problem
by: Bonhomme, Stéphane, et al.
Published: (2024)

Bayesian inference for Neyman-Scott point processes with anisotropic clusters
by: Dvořák, Jiří, et al.
Published: (2025)

Differentially private hypothesis testing in survival analysis
by: Hung, Elly K. H., et al.
Published: (2026)

Empirical Bayes factors for common hypothesis tests
by: Dudbridge, Frank
Published: (2023)

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)

Semi-parametric Bayesian inference under Neyman orthogonality
by: Sabbagh, Magid, et al.
Published: (2026)

A semi-supervised framework for diverse multiple hypothesis testing scenarios
by: Freestone, Jack, et al.
Published: (2024)

General linear hypothesis testing in ill-conditioned functional response model
by: Smaga, Łukasz, et al.
Published: (2024)

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models
by: López-Cheda, Ana, et al.
Published: (2024)

Rao-Blackwellized e-variables
by: de Roos, Dante, et al.
Published: (2025)

Simultaneous hypothesis testing for comparing many functional means
by: Decker, Colin, et al.
Published: (2025)

A generalized hypothesis test for community structure in networks
by: Yanchenko, Eric, et al.
Published: (2021)

The conditional saddlepoint approximation for fast and accurate large-scale hypothesis testing
by: Niu, Ziang, et al.
Published: (2024)

Doublethink: simultaneous Bayesian-frequentist model-averaged hypothesis testing
by: Fryer, Helen R., et al.
Published: (2023)

Active multiple testing with proxy p-values and e-values
by: Xu, Ziyu, et al.
Published: (2025)

Stronger Neyman Regret Guarantees for Adaptive Experimental Design
by: Noarov, Georgy, et al.
Published: (2025)

A comparative study of two-sample hypothesis tests in the presence of long-term survivors
by: Bi, Yu, et al.
Published: (2026)

How accurate are Bayes factor-based null hypothesis tests? A simulation study
by: Schad, Daniel J., et al.
Published: (2024)

Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators
by: Chen, Fangyi, et al.
Published: (2025)

Hypothesis testing with e-values
by: Ramdas, Aaditya, et al.
Published: (2024)

Besag-Clifford e-values for unnormalized testing
by: Dombowsky, Alexander, et al.
Published: (2026)

A unified Bayesian framework for interval hypothesis testing in clinical trials
by: Chakraborty, Abhisek, et al.
Published: (2024)

Causal generalized linear models via Pearson risk invariance
by: Polinelli, Alice, et al.
Published: (2024)