Danyang Huang,Xuening Zhu,Runze Li et al.
Danyang Huang et al.
Network analysis has drawn great attention in recent years. It is applied to a wide range disciplines. These include but are not limited to social science, finance and genetics. It is typical that one collects abundant covariates along the ...
Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference [0.03%]
非单调缺失数据的离散选择模型的识别与统计推断
Eric J Tchetgen Tchetgen,Linbo Wang,BaoLuo Sun
Eric J Tchetgen Tchetgen
Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is common to account for nonresponse under a missing-at-random ...
Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models [0.03%]
高维含峰模型样本相关矩阵的谱渐近性质研究
David Morales-Jimenez,Iain M Johnstone,Matthew R McKay et al.
David Morales-Jimenez et al.
Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply ran...
Wang Miao,Eric Tchetgen Tchetgen
Wang Miao
We study identification of parametric and semiparametric models with missing covariate data. When covariate data are missing not at random, identification is not guaranteed even under fairly restrictive parametric assumptions, a fact that i...
Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable [0.03%]
使用工具变量的缺失不随机半参数估计方法
BaoLuo Sun,Lan Liu,Wang Miao et al.
BaoLuo Sun et al.
Missing data occur frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, t...
Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules [0.03%]
基于多分类结果的边际损失个体化治疗策略估计方法
Chong Zhang,Jingxiang Chen,Haoda Fu et al.
Chong Zhang et al.
Due to heterogeneity for many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientific community. One main goal of precision medicine is to develop the most effecti...
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping [0.03%]
高维和大型空间数据的空问因子模型及在森林变量制图中的应用
Daniel Taylor-Rodriguez,Andrew O Finley,Abhirup Datta et al.
Daniel Taylor-Rodriguez et al.
Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiativ...
IDENTIFICATION AND INFERENCE FOR MARGINAL AVERAGE TREATMENT EFFECT ON THE TREATED WITH AN INSTRUMENTAL VARIABLE [0.03%]
工具变量下的边际处理效应的识别与推断
Lan Liu,Wang Miao,Baoluo Sun et al.
Lan Liu et al.
In observational studies, treatments are typically not randomized and therefore estimated treatment effects may be subject to confounding bias. The instrumental variable (IV) design plays the role of a quasi-experimental handle since the IV...
Feature Screening in Ultrahigh Dimensional Generalized Varying-coefficient Models [0.03%]
超高维广义变系数模型的变量筛选
Guangren Yang,Songshan Yang,Runze Li
Guangren Yang
Generalized varying coefficient models are particularly useful for examining dynamic effects of covariates on a continuous, binary or count response. This paper is concerned with feature screening for generalized varying coefficient models ...
Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers [0.03%]
时变风险模型在高维生物标志物不规则测量下的应用
Xiang Li,Quefeng Li,Donglin Zeng et al.
Xiang Li et al.
Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging cha...