Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning
From MaRDI portal
Publication:5057095
DOI10.1080/10618600.2021.2020128OpenAlexW4206373836MaRDI QIDQ5057095
Publication date: 15 December 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.07433
Neyman-Pearson lemmadeep neural networksconfirmatory adaptive clinical trialsefficient inference methodsresearch assistant tools
Uses Software
Cites Work
- Unnamed Item
- Nonlinear approximation via compositions
- Error bounds for approximations with deep ReLU networks
- On the asymptotic behaviour of the pseudolikelihood ratio test statistic with boundary problems
- Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome
- Frontiers of Statistical Decision Making and Bayesian Analysis
- Distribution-Free Two-Sample Tests for Scale
- Modification of Sample Size in Group Sequential Clinical Trials
- IX. On the problem of the most efficient tests of statistical hypotheses
- Computational Methods for Inverse Problems
- Evaluation of Experiments with Adaptive Interim Analyses
- Breaking the Curse of Dimensionality with Convex Neural Networks
- Testing Statistical Hypotheses
- ON THE COMPARISON OF SEVERAL MEAN VALUES: AN ALTERNATIVE APPROACH
- An Example of an Improvable Rao–Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator
This page was built for publication: Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning