On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models [0.03%]
高维变系数模型的变系数独立筛选法
Rui Song,Feng Yi,Hui Zou
Rui Song
Varying coefficient models have been widely used in longitudinal data analysis, nonlinear time series, survival analysis, and so on. They are natural non-parametric extensions of the classical linear models in many contexts, keeping good in...
Tyler J Vanderweele,James M Robins
Tyler J Vanderweele
Most work in causal inference concerns deterministic counterfactuals; the literature on stochastic counterfactuals is small. In the stochastic counterfactual setting, the outcome for each individual under each possible set of exposures foll...
Shuang Ji,Limin Peng,Ruosha Li et al.
Shuang Ji et al.
Dependent censoring occurs in many biomedical studies and poses considerable methodological challenges for survival analysis. In this work, we develop a new approach for analyzing dependently censored data by adopting quantile regression mo...
Variable Selection for Sparse High-Dimensional Nonlinear Regression Models by Combining Nonnegative Garrote and Sure Independence Screening [0.03%]
结合非负软阈值和SURE独立筛选的稀疏高维非线性回归模型变量选择方法研究
Shuang Wu,Hongqi Xue,Yichao Wu et al.
Shuang Wu et al.
In many regression problems, the relations between the covariates and the response may be nonlinear. Motivated by the application of reconstructing a gene regulatory network, we consider a sparse high-dimensional additive model with the add...
Ian W McKeague,Min Qian
Ian W McKeague
Biosignatures such as brain scans, mass spectrometry, or gene expression profiles might one day be used to guide treatment selection and improve outcomes. This article develops a way of estimating optimal treatment policies based on data fr...
Huaihou Chen,Yuanjia Wang,Runze Li et al.
Huaihou Chen et al.
We examine a test of a nonparametric regression function based on penalized spline smoothing. We show that, similarly to a penalized spline estimator, the asymptotic power of the penalized spline test falls into a small- K or a large-K scen...
Empirical Likelihood for Estimating Equations with Nonignorably Missing Data [0.03%]
非 ignorable 缺失数据下的估计方程的实证似然法
Niansheng Tang,Puying Zhao,Hongtu Zhu
Niansheng Tang
We develop an empirical likelihood (EL) inference on parameters in generalized estimating equations with nonignorably missing response data. We consider an exponential tilting model for the nonignorably missing mechanism, and propose modifi...
Eunhee Kim,Donglin Zeng
Eunhee Kim
The Receiver Operating Characteristic (ROC) curve is a widely used measure to assess the diagnostic accuracy of biomarkers for diseases. Biomarker tests can be affected by subject characteristics, the experience of testers, or the environme...
BAYESIAN WAVELET-BASED CURVE CLASSIFICATION VIA DISCRIMINANT ANALYSIS WITH MARKOV RANDOM TREE PRIORS [0.03%]
基于贝叶斯小波判别分析及马尔可夫随机场的曲线分类法
Francesco C Stingo,Marina Vannucci,Gerard Downey
Francesco C Stingo
Discriminant analysis is an effective tool for the classification of experimental units into groups. When the number of variables is much larger than the number of observations it is necessary to include a dimension reduction procedure into...
Hongtu Zhu,Joseph G Ibrahim,Niansheng Tang
Hongtu Zhu
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at variou...