Kuangnan Fang,Jingmao Li,Yaqing Xu et al.
Kuangnan Fang et al.
For the survival of cancer and many other complex diseases, gene-environment (G-E) interactions have been established as having essential importance. G-E interaction analysis can be roughly classified as marginal and joint, depending on the...
Xiaojun Mao,Zhonglei Wang,Shu Yang
Xiaojun Mao
Multivariate nonresponse is often encountered in complex survey sampling, and simply ignoring it leads to erroneous inference. In this paper, we propose a new matrix completion method for complex survey sampling. Different from existing wor...
Elena Pesce,Fabio Rapallo,Eva Riccomagno et al.
Elena Pesce et al.
After a rich history in medicine, randomized control trials (RCTs), both simple and complex, are in increasing use in other areas, such as web-based A/B testing and planning and design of decisions. A main objective of RCTs is to be able to...
Giorgos Bakoyannis,Dipankar Bandyopadhyay
Giorgos Bakoyannis
In this work, we propose nonparametric two-sample tests for population-averaged transition and state occupation probabilities for continuous-time and finite state space processes with clustered, right-censored, and/or left-truncated data. W...
Semiparametric modelling of two-component mixtures with stochastic dominance [0.03%]
含随机占优的两成分混合模型的半参数建模方法研究
Jingjing Wu,Tasnima Abedin,Qiang Zhao
Jingjing Wu
In this work, we studied a two-component mixture model with stochastic dominance constraint, a model arising naturally from many genetic studies. To model the stochastic dominance, we proposed a semiparametric modelling of the log of densit...
Weighted Estimating Equations for Additive Hazards Models with Missing Covariates [0.03%]
具有缺失协变量的加性风险模型的加权估计方程
Lihong Qi,Xu Zhang,Yanqing Sun et al.
Lihong Qi et al.
This paper presents simple weighted and fully augmented weighted estimators for the additive hazards model with missing covariates when they are missing at random. The additive hazards model estimates the difference in hazards and has an in...
Sparse and Efficient Estimation for Partial Spline Models with Increasing Dimension [0.03%]
维数渐大偏样条模型的稀疏高效估计
Guang Cheng,Hao Helen Zhang,Zuofeng Shang
Guang Cheng
We consider model selection and estimation for partial spline models and propose a new regularization method in the context of smoothing splines. The regularization method has a simple yet elegant form, consisting of roughness penalty on th...
Debdeep Pati,David B Dunson
Debdeep Pati
We consider the problem of robust Bayesian inference on the mean regression function allowing the residual density to change flexibly with predictors. The proposed class of models is based on a Gaussian process prior for the mean regression...
Xiaotong Shen,Wei Pan,Yunzhang Zhu et al.
Xiaotong Shen et al.
High-dimensional feature selection has become increasingly crucial for seeking parsimonious models in estimation. For selection consistency, we derive one necessary and sufficient condition formulated on the notion of degree-of-separation. ...
Simultaneous estimation and variable selection in median regression using Lasso-type penalty [0.03%]
Lasso型惩罚下的中位回归的估计与变量选择
Jinfeng Xu,Zhiliang Ying
Jinfeng Xu
We consider the median regression with a LASSO-type penalty term for variable selection. With the fixed number of variables in regression model, a two-stage method is proposed for simultaneous estimation and variable selection where the deg...