Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects [0.03%]
联邦自适应因果推断（FACE）在目标治疗效应中的应用
Larry Han,Jue Hou,Kelly Cho et al.
Larry Han et al.
Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federate...
Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment [0.03%]
基于设计的因果推理中的缺失结果处理：缺失机制、插补辅助随机化检验和协变量调整
Siyu Heng,Jiawei Zhang,Yang Feng
Siyu Heng
Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design...
Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State [0.03%]
具有吸收状态的区间截断多重状态数据的半参数回归分析
Yu Gu,Donglin Zeng,D Y Lin
Yu Gu
In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertai...
Local signal detection on irregular domains with generalized varying coefficient models [0.03%]
具有广义变系数模型的不规则区域局部信号检测方法研究
Chengzhu Zhang,Lan Xue,Yu Chen et al.
Chengzhu Zhang et al.
In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity ...
Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency in testing differential abundance [0.03%]
整合分析微生物16S基因和宏基因组测序数据可提高差异丰度检测的统计功效
Ye Yue,Yicong Mao,Timothy D Read et al.
Ye Yue et al.
The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. Th...
Zijian Guo,Xiudi Li,Larry Han et al.
Zijian Guo et al.
Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints. In this pap...
Cong Cheng,Yuan Ke,Wenyang Zhang
Cong Cheng
The estimation of large precision matrices is crucial in modern multivariate analysis. Traditional sparsity assumptions, while useful, often fall short of accurately capturing the dependencies among features. This paper addresses this limit...
Chien-Ming Chi,Yingying Fan,Ching-Kang Ing et al.
Chien-Ming Chi et al.
We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of sub...
Zhe Zhang,Xiufan Yu,Runze Li
Zhe Zhang
This paper proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential info...
Bangyao Zhao,Jane E Huggins,Jian Kang
Bangyao Zhao
Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electr...