Ya Su,Jill Reedy,Raymond J Carroll
Ya Su
This paper is dedicated to the memory of Peter G. Hall. It concerns a deceptively simple question: if one observes variables corrupted with measurement error of possibly very complex form, can one recreate asymptotically the clusters that w...
Bayesian Modeling and Inference for Nonignorably Missing Longitudinal Binary Response Data with Applications to HIV Prevention Trials [0.03%]
用于HIV预防试验的非忽略性缺失纵向二元反应数据的贝叶斯建模与推理方法
Jing Wu,Joseph G Ibrahim,Ming-Hui Chen et al.
Jing Wu et al.
Missing data are frequently encountered in longitudinal clinical trials. To better monitor and understand the progress over time, one must handle the missing data appropriately and examine whether the missing data mechanism is ignorable or ...
Functional Linear Regression Models for Nonignorable Missing Scalar Responses [0.03%]
非忽略缺失响应的函数线性回归模型
Tengfei Li,Fengchang Xie,Xiangnan Feng et al.
Tengfei Li et al.
As an important part of modern health care, medical imaging data, which can be regarded as densely sampled functional data, have been widely used for diagnosis, screening, treatment, and prognosis, such as finding breast cancer through mamm...
Asymptotic Behavior of Cox's Partial Likelihood and its Application to Variable Selection [0.03%]
Cox比例风险模型的部分似然及其在变量选择中的应用渐近性质
Runze Li,Jian-Jian Ren,Guangren Yang et al.
Runze Li et al.
For theoretical properties of variable selection procedures for Cox's model, we study the asymptotic behavior of partial likelihood for the Cox model. We find that the partial likelihood does not behave like an ordinary likelihood, whose sa...
A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials [0.03%]
缺失性假定偏离的敏感性分析的平均分值方法及其在随机对照试验中的应用
Ian R White,James Carpenter,Nicholas J Horton
Ian R White
Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approa...
ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL [0.03%]
带加性风险模型的最优治疗方案估计方法研究
Suhyun Kang,Wenbin Lu,Jiajia Zhang
Suhyun Kang
We propose a doubly robust estimation method for the optimal treatment regime based on an additive hazards model with censored survival data. Specifically, we introduce a new semiparametric additive hazard model which allows flexible baseli...
Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings [0.03%]
超高维设置下基于非局部先验的可扩展贝叶斯变量选择方法
Minsuk Shin,Anirban Bhattacharya,Valen E Johnson
Minsuk Shin
Bayesian model selection procedures based on nonlocal alternative prior densities are extended to ultrahigh dimensional settings and compared to other variable selection procedures using precision-recall curves. Variable selection procedure...
Siyuan Zhou,Debashis Paul,Jie Peng
Siyuan Zhou
We consider modeling non-autonomous dynamical systems for a group of subjects. The proposed model involves a common baseline gradient function and a multiplicative time-dependent subject-specific effect that accounts for phase and amplitude...
Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions [0.03%]
高维线性回归中的两样本检验及其在交互作用检测中的应用
Yin Xia,Tianxi Cai,T Tony Cai
Yin Xia
Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally i...
Predicting disease Risk by Transformation Models in the Presence of Unspecified Subgroup Membership [0.03%]
转换模型在亚组成员资格不明时对疾病风险的预测能力研究
Qianqian Wang,Yanyuan Ma,Yuanjia Wang
Qianqian Wang
Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific dis...