LOCAL BUCKLEY-JAMES ESTIMATION FOR HETEROSCEDASTIC ACCELERATED FAILURE TIME MODEL [0.03%]
异方差加速失效时间模型的局部Buckley-James估计方法
Lei Pang,Wenbin Lu,Huixia Judy Wang
Lei Pang
In survival analysis, the accelerated failure time model is a useful alternative to the popular Cox proportional hazards model due to its easy interpretation. Current estimation methods for the accelerated failure time model mostly assume i...
JOINT STRUCTURE SELECTION AND ESTIMATION IN THE TIME-VARYING COEFFICIENT COX MODEL [0.03%]
时间变化系数Cox模型的联合结构选择与估计
Wei Xiao,Wenbin Lu,Hao Helen Zhang
Wei Xiao
Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covaria...
Guangren Yang,Ye Yu,Runze Li et al.
Guangren Yang et al.
Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other fields. In this work, we propose a feature screening procedure for the Cox model with ultrahigh dimensional covaria...
Prediction-based Termination Rule for Greedy Learning with Massive Data [0.03%]
基于预测的贪婪学习终止规则在大数据中的应用研究
Chen Xu,Shaobo Lin,Jian Fang et al.
Chen Xu et al.
The appearance of massive data has become increasingly common in contemporary scientific research. When sample size n is huge, classical learning methods become computationally costly for the regression purpose. Recently, the orthogonal gre...
Hana Lee,Michael G Hudgens,Jianwen Cai et al.
Hana Lee et al.
A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weight...
BAYESIAN SPATIAL-TEMPORAL MODELING OF ECOLOGICAL ZERO-INFLATED COUNT DATA [0.03%]
生态零膨胀计数数据的贝叶斯时空建模方法研究
Xia Wang,Ming-Hui Chen,Rita C Kuo et al.
Xia Wang et al.
A Bayesian hierarchical model is developed for count data with spatial and temporal correlations as well as excessive zeros, uneven sampling intensities, and inference on missing spots. Our contribution is to develop a model on zero-inflate...
Philip S Boonstra,Bhramar Mukherjee,Jeremy M G Taylor
Philip S Boonstra
We propose new approaches for choosing the shrinkage parameter in ridge regression, a penalized likelihood method for regularizing linear regression coefficients, when the number of observations is small relative to the number of parameters...
Regularized Quantile Regression and Robust Feature Screening for Single Index Models [0.03%]
正则化分位数回归及单指标模型的稳健特征筛选方法
Wei Zhong,Liping Zhu,Runze Li et al.
Wei Zhong et al.
We propose both a penalized quantile regression and an independence screening procedure to identify important covariates and to exclude unimportant ones for a general class of ultrahigh dimensional single-index models, in which the conditio...
Sunyoung Shin,Jason Fine,Yufeng Liu
Sunyoung Shin
In many problems, one has several models of interest that capture key parameters describing the distribution of the data. Partially overlapping models are taken as models in which at least one covariate effect is common to the models. A pri...
BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES [0.03%]
具有并列结果的生存数据的隐含伽马损耗过程模型的贝叶斯推断方法研究
Arijit Sinha,Zhiyi Chi,Ming-Hui Chen
Arijit Sinha
Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a ...