Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study [0.03%]
基于随机对照试验的治疗效应估计的稳健性分析变异方法
Fei Jiang,Lu Tian,Haoda Fu et al.
Fei Jiang et al.
In comparing two treatments via a randomized clinical trial, the analysis of covariance (ANCOVA) technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its s...
Yize Zhao,Changgee Chang,Jingwen Zhang et al.
Yize Zhao et al.
With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain s...
A general framework for inference on algorithm-agnostic variable importance [0.03%]
一种基于算法不可知的变量重要性推理方法框架
Brian D Williamson,Peter B Gilbert,Noah R Simon et al.
Brian D Williamson et al.
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response - in other words, to gauge the variable importance of features. Most recent work on va...
Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data [0.03%]
基于个体数据保护的高维异构数据分析集成回归方法研究
Tianxi Cai,Molei Liu,Yin Xia
Tianxi Cai
Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challeng...
Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data [0.03%]
具无效工具变量和GWAS汇总数据的转录组-wide关联研究中的因果推断
Haoran Xue,Xiaotong Shen,Wei Pan
Haoran Xue
Transcriptome-wide association studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our m...
Xiongtao Dai,Sara Lopez-Pintado;Alzheimer’s Disease Neuroimaging Initiative
Xiongtao Dai
We develop a novel exploratory tool for non-Euclidean object data based on data depth, extending celebrated Tukey's depth for Euclidean data. The proposed metric halfspace depth, applicable to data objects in a general metric space, assigns...
Discussion of "LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures" [0.03%]
"LES阿弹性形状分析脑皮下结构的发展讨论"
Moo K Chung,Jamie L Hanson,Richard J Davidson et al.
Moo K Chung et al.
Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference [0.03%]
稀疏主题建模：计算效率、准最优算法和统计推理
Ruijia Wu,Linjun Zhang,T Tony Cai
Ruijia Wu
Sparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed ...
Serge Aleshin-Guendel,Mauricio Sadinle
Serge Aleshin-Guendel
Merging datafiles containing information on overlapping sets of entities is a challenging task in the absence of unique identifiers, and is further complicated when some entities are duplicated in the datafiles. Most approaches to this prob...
Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration [0.03%]
通过纵向生物标志物登记进行未知时间起源的时间事件分析
Tianhao Wang,Sarah J Ratcliffe,Wensheng Guo
Tianhao Wang
In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longit...