Jianqing Fan,Cong Ma,Yiqiao Zhong
Jianqing Fan
Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a l...
Network Modeling in Biology: Statistical Methods for Gene and Brain Networks [0.03%]
生物学中的网络模型构建：基因及脑网络的统计方法
Y X Rachel Wang,Lexin Li,Jingyi Jessica Li et al.
Y X Rachel Wang et al.
The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In t...
Corwin M Zigler,Georgia Papadogeorgou
Corwin M Zigler
Statistical methods to evaluate the effectiveness of interventions are increasingly challenged by the inherent interconnectedness of units. Specifically, a recent flurry of methods research has addressed the problem of interference between ...
Susan M Shortreed,Erica E M Moodie
Susan M Shortreed
Anna L Smith,Dena M Asta,Catherine A Calder
Anna L Smith
We review the class of continuous latent space (statistical) models for network data, paying particular attention to the role of the geometry of the latent space. In these models, the presence/absence of network dyadic ties are assumed to b...
Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study [0.03%]
含内生协变量的线性混合模型及在序贯治疗效应与移动健康研究中的应用
Tianchen Qian,Predrag Klasnja,Susan A Murphy
Tianchen Qian
Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for ...
David Whitney,Ali Shojaie,Marco Carone
David Whitney
Adam R Brentnall,Jack Cuzick
Adam R Brentnall
Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in ...
Paul J Birrell,Daniela De Angelis,Anne M Presanis
Paul J Birrell
In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of i...
Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions [0.03%]
贝叶斯缺失不随机结果数据处理方法：识别限制的作用
Antonio R Linero,Michael J Daniels
Antonio R Linero
Missing data is almost always present in real datasets, and introduces several statistical issues. One fundamental issue is that, in the absence of strong uncheckable assumptions, effects of interest are typically not nonparametrically iden...