Tyler J. VanderWeele's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng [0.03%]
泰勒·J·范德维利对罗赫和曾格论文《古典因素分析与瓦尔米克斯最大化实现统计推断》的评论贡献
Tyler J VanderWeele
Tyler J VanderWeele
A focusing framework for testing bi-directional causal effects in Mendelian randomization [0.03%]
一种用于梅纳德尔随机化中双向因果效应检验的聚焦框架
Sai Li,Ting Ye
Sai Li
Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables to infer the causal effect of a modifiable exposure on an outcome. We study inference for bi-directional causal relationships and causal ...
Xiaowu Dai
Xiaowu Dai
Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either obs...
Chris J Oates,Toni Karvonen,Aretha L Teckentrup et al.
Chris J Oates et al.
For over a century, extrapolation methods have provided a powerful tool to improve the convergence order of a numerical method. However, these tools are not well-suited to modern computer codes, where multiple continua are discretized and c...
Yaowu Liu,Zhonghua Liu,Xihong Lin
Yaowu Liu
Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of...
Mode-wise principal subspace pursuit and matrix spiked covariance model [0.03%]
模式正交主子空间 Pursuit 和矩阵奇异协方差模型
Runshi Tang,Ming Yuan,Anru R Zhang
Runshi Tang
This paper introduces a novel framework called Mode-wise Principal Subspace Pursuit (MOP-UP) to extract hidden variations in both the row and column dimensions for matrix data. To enhance the understanding of the framework, we introduce a c...
Paula Gablenz,Chiara Sabatti
Paula Gablenz
We consider problems where many, somewhat redundant, hypotheses are tested and we are interested in reporting the most precise rejections, with false discovery rate (FDR) control. This is the case, for example, when researchers are interest...
Extended fiducial inference: toward an automated process of statistical inference [0.03%]
扩展的似然推断——迈向统计推断自动化过程
Faming Liang,Sehwan Kim,Yan Sun
Faming Liang
While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set-'inferring the uncertainty of model parameters on the basis of observations'-has been continually pursued by many statisticians. To this ...
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation [0.03%]
通过正则化校准估计的未测量混淆下的治疗效应的模型辅助敏感性分析
Zhiqiang Tan
Zhiqiang Tan
Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds an...
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease [0.03%]
一种解释性判别分析及其在阿尔茨海默病研究中的应用
Eardi Lila,Wenbo Zhang,Swati Rane Levendovszky;Alzheimer’s Disease Neuroimaging Initiative
Eardi Lila
We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortica...