Zack W Almquist,Carter T Butts
Zack W Almquist
Statistical methods for dynamic network analysis have advanced greatly in the past decade. This article extends current estimation methods for dynamic network logistic regression (DNR) models, a subfamily of the Temporal Exponential-family ...
Yongli Zhang,Xiaotong Shen,Shaoli Wang
Yongli Zhang
Large economic and financial networks may experience stage-wise changes as a result of external shocks. To detect and infer a structural change, we consider an inference problem within a framework of multiple Gaussian Graphical Models when ...
Multi-response Regression for Block-missing Multi-modal Data without Imputation [0.03%]
无插补处理缺失的多模态数据的多响应回归方法
Haodong Wang,Quefeng Li,Yufeng Liu
Haodong Wang
Multi-modal data are prevalent in many scientific fields. In this study, we consider the parameter estimation and variable selection for a multi-response regression using block-missing multi-modal data. Our method allows the dimensions of b...
UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS [0.03%]
基于负控制去除不需要的变化的方法的统一和推广
David Gerard,Matthew Stephens
David Gerard
Unwanted variation, including hidden confounding, is a well-known problem in many fields, but particularly in large-scale gene expression studies. Recent proposals to use control genes, genes assumed to be unassociated with the covariates o...
EFFICIENT AND ROBUST ESTIMATION OF τ-YEAR RISK PREDICTION MODELS LEVERAGING TIME VARYING INTERMEDIATE OUTCOMES [0.03%]
高效且稳健的τ年风险预测模型估计方法兼用中间结局的时变信息
Yu Zheng,Tian Lu,Tianxi Cai
Yu Zheng
Accurate risk prediction models play a key role in precision medicine, where optimal individualized disease prevention and treatment strategies can be formed based on predicted risks. In many clinical settings, it is of great interest to pr...
HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES [0.03%]
基于多源高维数据集成的异质性分析及其在癌症研究中的应用
Tingyan Zhong,Qingzhao Zhang,Jian Huang et al.
Tingyan Zhong et al.
This study has been motivated by cancer research, in which heterogeneity analysis plays an important role and can be roughly classified as unsupervised or supervised. In supervised heterogeneity analysis, the finite mixture of regression (F...
Peiyao Wang,Quefeng Li,Dinggang Shen et al.
Peiyao Wang et al.
In modern scientific research, data heterogeneity is commonly observed owing to the abundance of complex data. We propose a factor regression model for data with heterogeneous subpopulations. The proposed model can be represented as a decom...
Use of random integration to test equality of high dimensional covariance matrices [0.03%]
随机积分在检验高维协方差阵的相等性中的应用
Yunlu Jiang,Canhong Wen,Yukang Jiang et al.
Yunlu Jiang et al.
Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional c...
Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates [0.03%]
高维组成协变量的全局适应纵向分位数回归
Huijuan Ma,Qi Zheng,Zhumin Zhang et al.
Huijuan Ma et al.
In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurem...
An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis [0.03%]
高维线性判别分析的高效贪婪搜索算法
Hannan Yang,D Y Lin,Quefeng Li
Hannan Yang
High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been propo...