Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes [0.03%]
具有因素结构结果的研究中未观察到的混淆的敏感性
Jiajing Zheng,Jiaxi Wu,Alexander D&#x;Amour et al.
Jiajing Zheng et al.
In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclus...
Linquan Ma,Jixin Wang,Han Chen et al.
Linquan Ma et al.
The estimation of the central space is at the core of the sufficient dimension reduction (SDR) literature. However, it is well known that the finite-sample estimation suffers from collinearity among predictors. Cook et al. (2013) proposed t...
Addressing Multiple Detection Limits with Semiparametric Cumulative Probability Models [0.03%]
用半参数累积概率模型处理多个检测限问题
Yuqi Tian,Chun Li,Shengxin Tu et al.
Yuqi Tian et al.
Detection limits (DLs), where a variable cannot be measured outside of a certain range, are common in research. DLs may vary across study sites or over time. Most approaches to handling DLs in response variables implicitly make strong param...
Discussion on "Regression Models for Understanding COVID-19 Epidemic Dynamics with Incomplete Data" [0.03%]
关于《利用数据不完整性的COVID-19流行病动力学回归模型理解》的讨论
Jyotishka Datta,Bhramar Mukherjee
Jyotishka Datta
Alan E Gelfand,Hyon-Jung Kim,C F Sirmans et al.
Alan E Gelfand et al.
In many applications, the objective is to build regression models to explain a response variable over a region of interest under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coeffici...
Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning [0.03%]
基于结构、监督和生成对抗的有向无环图测试
Chengchun Shi,Yunzhe Zhou,Lexin Li
Chengchun Shi
In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods ofte...
Balancing Inferential Integrity and Disclosure Risk via Model Targeted Masking and Multiple Imputation [0.03%]
基于模型的掩码和多重插补在维护推断完整性和披露风险之间的平衡中的应用研究
Bei Jiang,Adrian E Raftery,Russell J Steele et al.
Bei Jiang et al.
There is a growing expectation that data collected by government-funded studies should be openly available to ensure research reproducibility, and so is the concern on data-privacy. A strategy to protect individuals' identity is to release ...
Heterogeneity Analysis on Multi-state Brain Functional Connectivity and Adolescent Neurocognition [0.03%]
多模态脑功能连接的异质性分析及其与青少年神经心理水平之间的关联研究
Shiying Wang,Todd Constable,Heping Zhang et al.
Shiying Wang et al.
Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concen...
Lili Zheng,Genevera I Allen
Lili Zheng
In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vas...
Caleb Ki,Jonathan Terhorst
Caleb Ki
In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genet...