Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data [0.03%]
基于贝叶斯分组因子模型的多张计数表数据特征交互作用稀疏化方法
Shuangjie Zhang,Yuning Shen,Irene A Chen et al.
Shuangjie Zhang et al.
Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develo...
Xu Guo,Runze Li,Zhe Zhang et al.
Xu Guo et al.
This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propos...
Inferring independent sets of Gaussian variables after thresholding correlations [0.03%]
阈值化处理相关性后的高斯变量的独立集的推理方法
Arkajyoti Saha,Daniela Witten,Jacob Bien
Arkajyoti Saha
We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables for which the correlation with all other va...
Mediation analysis with the mediator and outcome missing not at random [0.03%]
结局和中介变量缺失非随机情况下的中介分析方法研究
Shuozhi Zuo,Debashis Ghosh,Peng Ding et al.
Shuozhi Zuo et al.
Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the mediator and outcome. In general, when the...
Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies [0.03%]
利用条件对称多维高斯混合检验基因组研究中的多个复合零假设
Ryan Sun,Zachary R McCaw,Xihong Lin
Ryan Sun
Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inference problems all involve large-scale tes...
Seong-Ho Lee,Yanyuan Ma,Jiwei Zhao
Seong-Ho Lee
In studies ranging from clinical medicine to policy research, complete data are usually available from a population 𝒫 , but the quantity of interest is often sought for a related but different population 𝒬 which only has par...
Tianxi Cai,Mengyan Li,Molei Liu
Tianxi Cai
In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce target population and utilizes a large amoun...
Distributed Heterogeneity Learning for Generalized Partially Linear Models with Spatially Varying Coefficients [0.03%]
空间变化系数广义部分线性模型的分布式异质性学习方法研究
Shan Yu,Guannan Wang,Li Wang
Shan Yu
Spatial heterogeneity is of great importance in social, economic, and environmental science studies. The spatially varying coefficient model is a popular and effective spatial regression technique to address spatial heterogeneity. However, ...
Runze Li,Weiming Li,Qinwen Wang
Runze Li
Tyler's M estimator, as a robust alternative to the sample covariance matrix, has been widely applied in robust statistics. However, classical theory on Tyler's M estimator is mainly developed in the low-dimensional regime for elliptical po...
Comment on "Data Fission: Splitting a Single Data Point", Data Fission for Unsupervised Learning: A Discussion on Post-Clustering Inference and the Challenges of Debiasing [0.03%]
关于“数据分裂：拆分单个数据点”的评论，无监督学习中的数据分裂：一次聚类推理的讨论及去偏挑战的探讨
Changhu Wang,Xinzhou Ge,Dongyuan Song et al.
Changhu Wang et al.