Multiple testing, uncertainty and realistic pictures

博弈论概率中的多重检验：图景与问题

Multiple testing in game-theoretic probability: pictures and questions

本文探讨博弈论概率中由多个预测者输出预测时的多重检验问题，发现略微放宽族系有效性可显著提升检验效率，并报告初步模拟结果与未来研究方向。

假设检验中的 e 值

Hypothesis testing with e-values

本书系统论述假设检验中的 e 值，分三部分涵盖基本概念、核心思想与高级专题，汇集现代研究成果，可作为研究生课程基础。

New testing procedures for Structural Equation Modeling

We introduce and evaluate a new class of hypothesis testing procedures for moment structures. The methods are valid under weak assumptions and includes the well-known Satorra-Bentler adjustment as a special case. The proposed procedures applies also to difference testing among nested models. We prove the consistency of our approach. We introduce a bootstrap selection mechanism to optimally choose a p-value approximation for a given sample. Also, we propose bootstrap procedures for assessing the asymptotic robustness (AR) of the normal-theory maximum likelihood test, and for the key assumption …

Hierarchical Testing in the High-Dimensional Setting with Correlated Variables

We propose a method for testing whether hierarchically ordered groups of potentially correlated variables are significant for explaining a response in a high-dimensional linear model. In presence of highly correlated variables, as is very common in high-dimensional data, it seems indispensable to go beyond an approach of inferring individual regression coefficients, and we show that detecting smallest groups of variables (MTDs: minimal true detections) is realistic. Thanks to the hierarchy among the groups of variables, powerful multiple testing adjustment is possible which leads to a data-dri…

A nonparametric empirical Bayes framework for large-scale multiple testing

We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-null cases. We use a computationally efficient predictive recursion marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real-data examples demonstrate that, compared to existing…

不等规模大型随机图的双样本假设检验

Two-Sample Hypothesis Testing for Large Random Graphs of Unequal Size

本文提出针对小样本和不等规模随机图的Frobenius检验统计量，通过从估计的社区边概率矩阵生成自助邻接矩阵构建检验基础，推导其渐近分布，并在模拟和fMRI数据中验证了其稳定性和有效性。