Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach [0.03%]
Cox模型下的风险估计中适应时变异质性的迁移学习方法研究
Ziyi Li,Yu Shen,Jing Ning
Ziyi Li
Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy ...
Alessandro Zito,Tommaso Rigon,Otso Ovaskainen et al.
Alessandro Zito et al.
We aim at modeling the appearance of distinct tags in a sequence of labeled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarized via accum...
Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data [0.03%]
低秩回归模型在癌症细胞系百科全书数据中的多二元响应应用研究
Seyoung Park,Eun Ryung Lee,Hongyu Zhao
Seyoung Park
In this paper, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies wi...
Optimal Estimation of Genetic Relatedness in High-dimensional Linear Models [0.03%]
高维线性模型中基因相关性的最优估计方法
Zijian Guo,Wanjie Wang,T Tony Cai et al.
Zijian Guo et al.
Estimating the genetic relatedness between two traits based on the genome-wide association data is an important problem in genetics research. In the framework of high-dimensional linear models, we introduce two measures of genetic relatedne...
Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes [0.03%]
混合响应状态空间模型用于分析多维数字表型
Tianchen Xu,Yuan Chen,Donglin Zeng et al.
Tianchen Xu et al.
Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to conf...
Power and multicollinearity in small networks: A discussion of "Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks" by Krivitsky, Coletti & Hens [0.03%]
小网络中的权力和多重共线性：“两个数据集的故事：Krivitsky，Coletti & Hens样本网络推断的代表性与普遍性”讨论
George G Vega Yon
George G Vega Yon
The recent work by Krivitsky, Coletti & Hens [KCH] provides an important new contribution to the Exponential-Family Random Graph Models [ERGMs], a start-to-finish approach to dealing with multi-network ERGMs. Although multi-network ERGMs ha...
Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models [0.03%]
高维非平稳VAR模型的联合结构突变检测和参数估计
Abolfazl Safikhani,Ali Shojaie
Abolfazl Safikhani
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for si...
Kean Ming Tan,Qiang Sun,Daniela Witten
Kean Ming Tan
We propose a sparse reduced rank Huber regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank- and sparsity-constrained nonconvex optimi...
Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data [0.03%]
评估最易受他汀类药物使用相关二型糖尿病影响的亚群体：来自电子健康记录数据的证据
Xinzhou Guo,Waverly Wei,Molei Liu et al.
Xinzhou Guo et al.
There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset Type II diabetes (T2D). Nevertheless,...