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...
Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials [0.03%]
演化状态空间模型及其在局部场电位的时间频率分析中的应用
Xu Gao,Weining Shen,Babak Shahbaba et al.
Xu Gao et al.
We propose an evolutionary state space model (E-SSM) for analyzing high dimensional brain signals whose statistical properties evolve over the course of a non-spatial memory experiment. Under E-SSM, brain signals are modeled as mixtures of ...
The Lq- NORM LEARNING FOR ULTRAHIGH-DIMENSIONAL SURVIVAL DATA: AN INTEGRATIVE FRAMEWORK [0.03%]
超⾼维生存数据的Lq范数学习：整合框架
H G Hong,X Chen,J Kang et al.
H G Hong et al.
In the era of precision medicine, survival outcome data with high-throughput predictors are routinely collected. Models with an exceedingly large number of covariates are either infeasible to fit or likely to incur low predictability becaus...
Tram Ta,Jun Shao,Quefeng Li et al.
Tram Ta et al.
Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a surv...
TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS [0.03%]
张量广义估计方程在纵向图像分析中的应用
Xiang Zhang,Lexin Li,Hua Zhou et al.
Xiang Zhang et al.
Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are...