:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Krause, Robert W.
Format:	Preprint
Published:	2026
Subjects:	Methodology
Online Access:	https://arxiv.org/abs/2604.22051
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multivariate Interval-Valued Models in Frequentist and Bayesian Schemes
by: Sadeghkhani, Ali, et al.
Published: (2024)

Bayesian and Frequentist Inference for Synthetic Controls
by: Martinez, Ignacio, et al.
Published: (2022)

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

Frequentist properties of Bayesian inequality tests
by: Kaplan, David M., et al.
Published: (2016)

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

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

Empirical Bayes for the Reluctant Frequentist
by: Koenker, Roger, et al.
Published: (2024)

Polynomial Maximization Method with Fractional Polynomial Basis: A Frequentist Bridge to Bayesian Fractional Polynomials
by: Zabolotnii, Serhii
Published: (2026)

Frequentist Inference for Semi-mechanistic Epidemic Models with Interventions
by: Bong, Heejong, et al.
Published: (2023)

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators
by: Iba, Yukito
Published: (2023)

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

Frequentist Statistics as Internalist Reliabilism
by: Lin, Hanti
Published: (2024)

Error Bounds Revisited, and How to Use Bayesian Statistics While Remaining a Frequentist
by: Xu, Ning, et al.
Published: (2025)

Implementing Errors on Errors: Bayesian vs Frequentist
by: Mishima, Satoshi, 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)

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)

Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata
by: Wesołowski, Jacek, et al.
Published: (2023)

Combining Bayesian and Frequentist Inference for Laboratory-Specific Performance Guarantees in Copy Number Variation Detection
by: Talbot, Austin, et al.
Published: (2026)

Prevalence estimation in infectious diseases with imperfect tests: A comparison of Frequentist and Bayesian Logistic Regression methods with misclassification correction
by: Alvarez, Jorge Mario Estrada, et al.
Published: (2025)

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

Bayesian Covariance Structure Modeling of Multi-Way Nested Data
by: Baas, Stef, et al.
Published: (2022)

A Frequentist Statistical Introduction to Variational Inference, Autoencoders, and Diffusion Models
by: Chen, Yen-Chi
Published: (2025)

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

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

Beyond Neyman-Pearson: e-values enable hypothesis testing with a data-driven alpha
by: Grünwald, Peter
Published: (2022)

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

Neyman Jackknife: Design-Based Variance Estimation for Causal Inference under Interference
by: Park, Bryan, et al.
Published: (2026)

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
by: Li, Michael Lingzhi, et al.
Published: (2024)

Clustering Three-Way Data with Outliers
by: Clark, Katharine M., et al.
Published: (2023)

Rejoinder to the discussants of the two JASA articles `Frequentist Model Averaging' and `The Focused Information Criterion', by Nils Lid Hjort and Gerda Claeskens
by: Hjort, Nils Lid, et al.
Published: (2026)

Estimation of Directed Acyclic Graphs by Frequentist Model Averaging
by: Liu, Huihang, et al.
Published: (2026)

False Discovery Rate Control via Frequentist-assisted Horseshoe
by: Liang, Qiaoyu, et al.
Published: (2025)

Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
by: Kato, Masahiro
Published: (2024)

Bayesian Inference of Geometric Brownian Motion: An Extension with Jumps
by: Yan, Yifei, et al.
Published: (2025)

The Bayesian Way: Uncertainty, Learning, and Statistical Reasoning
by: Sosa, Juan, et al.
Published: (2025)

A Comparison of the Bayesian Posterior Probability and the Frequentist $p$-Value in Testing Equivalence Hypotheses
by: Ochieng, Daniel
Published: (2025)

An Efficient Sampling from Circular Distributions and its Extension to Toroidal Distributions
by: Biswas, Surojit, et al.
Published: (2024)

Bayesian Hierarchical Modelling of Noisy Gamma Processes: Model Formulation, Identifiability, Model Fitting, and Extensions to Unit-to-Unit Variability
by: Leadbetter, Ryan, et al.
Published: (2024)