Variable selection for recurrent event data with broken adaptive ridge regression [0.03%]
断裂自适应岭回归在复发事件数据中的变量选择问题
Hui Zhao,Dayu Sun,Gang Li et al.
Hui Zhao et al.
Recurrent event data occur in many areas such as medical studies and social sciences and a great deal of literature has been established for their analysis. On the other hand, only limited research exists on the variable selection for recur...
Synthetic data method to incorporate external information into a current study [0.03%]
合成数据方法，将外部信息纳入当前研究中
Tian Gu,Jeremy M G Taylor,Wenting Cheng et al.
Tian Gu et al.
We consider the situation where there is a known regression model that can be used to predict an outcome, Y, from a set of predictor variables X. A new variable B is expected to enhance the prediction of Y. A dataset of size n containing Y,...
Analysis of generalized semiparametric mixed varying-coefficients models for longitudinal data [0.03%]
纵向数据分析的广义半参数混合变系数模型及其应用研究
Yanqing Sun,Li Qi,Fei Heng et al.
Yanqing Sun et al.
The generalized semiparametric mixed varying-coefficient effects model for longitudinal data can accommodate a variety of link functions and flexibly model different types of covariate effects, including time-constant, time-varying, and cov...
A semiparametric linear transformation model to estimate causal effects for survival data [0.03%]
半参数线性变换模型在生存数据因果效应估计中的应用研究
Huazhen Lin,Yi Li,Liang Jiang et al.
Huazhen Lin et al.
Semiparametric linear transformation models serve as useful alternatives to the Cox proportional hazard model. In this study, we use the semiparametric linear transformation model to analyze survival data with selective compliance. We estim...
Yizheng Wei,Yanyuan Ma,Tanya P Garcia et al.
Yizheng Wei et al.
We propose a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and ...
Farouk S Nathoo,Linglong Kong,Hongtu Zhu
Farouk S Nathoo
With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. S...
Yi Zhai,Zhide Fang
Yi Zhai
In this paper, we consider the problem of seeking locally optimal designs for nonlinear dose-response models with binary outcomes. Applying the theory of Tchebycheff Systems and other algebraic tools, we show that the locally D-, A-, and c-...
Jonathan Taylor,Robert Tibshirani
Jonathan Taylor
We present a new method for post-selection inference for ℓ1 (lasso)-penalized likelihood models, including generalized regression models. Our approach generalizes the post-selection framework presented in Lee et al. (2013). The method prov...
Chun Wang,Ming-Hui Chen,Jing Wu et al.
Chun Wang et al.
For big data arriving in streams, online updating is an important statistical method that breaks the storage barrier and the computational barrier under certain circumstances. In the regression context, online updating algorithms assume tha...
Estimating treatment effects in observational studies with both prevalent and incident cohorts [0.03%]
利用现症和新发对照估计观察性研究中的处理效应
Jing Ning,Chuan Hong,Liang Li et al.
Jing Ning et al.
Registry databases are increasingly being used for comparative effectiveness research in cancer. Such databases reflect the real-world patient population and physician practice, and thus are natural sources for comparing multiple treatment ...