Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods [0.03%]
Vecchia近似下的固定域渐近性质
Lu Zhang,Wenpin Tang,Sudipto Banerjee
Lu Zhang
Statistical modeling for massive spatial data sets has generated a substantial literature on scalable spatial processes based upon Vecchia's approximation. Vecchia's approximation for Gaussian process models enables fast evaluation of the l...
PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS [0.03%]
带测量误差的条件部分功能线性分位数回归模型估计方法研究
Mengli Zhang,Lan Xue,Carmen D Tekwe et al.
Mengli Zhang et al.
Ignoring measurement errors in conventional regression analyses can lead to biased estimation and inference results. Reducing such bias is challenging when the error-prone covariate is a functional curve. In this paper, we propose a new cor...
Jaffer M Zaidi,Tyler J VanderWeele
Jaffer M Zaidi
The sufficient cause model is extended from binary to categorical and ordinal outcomes to formalize the concept of sufficient cause interaction and synergism in this setting. This extension allows us to derive counterfactual and empirical c...
ESTIMATION FOR EXTREME CONDITIONAL QUANTILES OF FUNCTIONAL QUANTILE REGRESSION [0.03%]
函数型分位数回归的极端条件下的分位点估计方法研究
Hanbing Zhu,Riquan Zhang,Yehua Li et al.
Hanbing Zhu et al.
Quantile regression as an alternative to modeling the conditional mean function provides a comprehensive picture of the relationship between a response and covariates. It is particularly attractive in applications focused on the upper or lo...
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...