Post-selection inference for high-dimensional mediation analysis with survival outcomes [0.03%]
高维中介分析中生存结果的事后推断
Tzu-Jung Huang,Zhonghua Liu,Ian W McKeague
Tzu-Jung Huang
It is of substantial scientific interest to detect mediators that lie in the causal pathway from an exposure to a survival outcome. However, with high-dimensional mediators, as often encountered in modern genomic data settings, there is a l...
Jianrui Zhang,Chenxi Li,Haolei Weng
Jianrui Zhang
We develop a post-selection inference method for the Cox proportional hazards model with interval-censored data, which provides asymptotically valid p-values and confidence intervals conditional on the model selected by lasso. The method is...
Tommaso Rigon,Sonia Petrone,Bruno Scarpa
Tommaso Rigon
Bayesian nonparametrics has evolved into a broad area encompassing flexible methods for Bayesian inference, combinatorial structures, tools for complex data reduction, and more. Discrete prior laws play an important role in these developmen...
A New Paradigm for High-dimensional Data: Distance-Based Semiparametric Feature Aggregation Framework via Between-Subject Attributes [0.03%]
一种处理高维数据的新范式：基于受试者间特征的距离半参数特征聚合框架
Jinyuan Liu,Xinlian Zhang,Tuo Lin et al.
Jinyuan Liu et al.
This article proposes a distance-based framework incentivized by the paradigm shift towards feature aggregation for high-dimensional data, which does not rely on the sparse-feature assumption or the permutation-based inference. Focusing on ...
Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies [0.03%]
最小化最大功能协方差分析检验：在纵向全基因组关联研究中的应用
Weicheng Zhu,Sheng Xu,Catherine Liu et al.
Weicheng Zhu et al.
We model the Alzheimer's Disease-related phenotype response variables observed on irregular time points in longitudinal Genome-Wide Association Studies as sparse functional data and propose nonparametric test procedures to detect functional...
Testing the missing at random assumption in generalized linear models in the presence of instrumental variables [0.03%]
广义线性模型中工具变量存在时的随机缺失假定的检验
Rui Duan,C Jason Liang,Pamela A Shaw et al.
Rui Duan et al.
Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing...
On the identification of individual level pleiotropic, pure direct, and principal stratum direct effects without cross world assumptions [0.03%]
个体水平的多效性、纯粹直接效应及主层直接效应在无跨世界假设下的识别问题研究
Jaffer M Zaidi,Tyler J VanderWeele
Jaffer M Zaidi
The analysis of natural direct and principal stratum direct effects has a controversial history in statistics and causal inference as these effects are commonly identified with either untestable cross world independence or graphical assumpt...
Generalizing the information content for stepped wedge designs: A marginal modeling approach [0.03%]
推广阶梯楔形设计的信息内容：边际建模方法
Fan Li,Jessica Kasza,Elizabeth L Turner et al.
Fan Li et al.
Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for ...
Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates [0.03%]
Cox比例 Hazard模型的条件准最大似然估计及其推断（渐近性质）
Lu Xia,Bin Nan,Yi Li
Lu Xia
For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violate...