A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion [0.03%]
一个新的半参数回归混合模型通过融合惩罚回归实现
Erin Austin,Wei Pan,Xiaotong Shen
Erin Austin
For some modeling problems a population may be better assessed as an aggregate of unknown subpopulations, each with a distinct relationship between a response and associated variables. The finite mixture of regressions (FMR) model, in which...
Walter Dempsey
Walter Dempsey
We consider exchangeable Markov multi-state survival processes, which are temporal processes taking values over a state-space S , with at least one absorbing failure state b ∈ S that satisfy the natural invariance properties of excha...
AN EM COMPOSITE LIKELIHOOD APPROACH FOR MULTISTAGE SAMPLING OF FAMILY DATA [0.03%]
多阶段家庭数据采样的复合似然方法研究
Y Choi,L Briollais
Y Choi
Multistage sampling of family data is a common design in the field of genetic epidemiology, but appropriate methodologies for analyzing data collected under this design are still lacking. We propose here a statistical approach based on the ...
Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect [0.03%]
平均因果效应的可行和稳健估计的充分维数约简
Trinetri Ghosh,Yanyuan Ma,Xavier de Luna
Trinetri Ghosh
When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and non-treat...
A COPULA-MODEL BASED SEMIPARAMETRIC INTERACTION TEST UNDER THE CASE-CONTROL DESIGN [0.03%]
一种基于库普勒模型的半参数病例对照数据交互效应检验方法
Hong Zhang,Jing Qin,Maria Landi et al.
Hong Zhang et al.
It is important to study the interaction between two risk factors in molecular epidemiology studies. To improve the power for the detection of interaction, some statistical testing procedures have been proposed in the literature by incorpor...
Generalized scale-change models for recurrent event processes under informative censoring [0.03%]
具有效应截断的复发事件过程的广义比例刻度模型
Gongjun Xu,Sy Han Chiou,Jun Yan et al.
Gongjun Xu et al.
Two major challenges arise in regression analyses of recurrent event data: first, popular existing models, such as the Cox proportional rates model, may not fully capture the covariate effects on the underlying recurrent event process; seco...
Danyang Huang,Xuening Zhu,Runze Li et al.
Danyang Huang et al.
Network analysis has drawn great attention in recent years. It is applied to a wide range disciplines. These include but are not limited to social science, finance and genetics. It is typical that one collects abundant covariates along the ...
Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference [0.03%]
非单调缺失数据的离散选择模型的识别与统计推断
Eric J Tchetgen Tchetgen,Linbo Wang,BaoLuo Sun
Eric J Tchetgen Tchetgen
Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is common to account for nonresponse under a missing-at-random ...
Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models [0.03%]
高维含峰模型样本相关矩阵的谱渐近性质研究
David Morales-Jimenez,Iain M Johnstone,Matthew R McKay et al.
David Morales-Jimenez et al.
Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply ran...
Wang Miao,Eric Tchetgen Tchetgen
Wang Miao
We study identification of parametric and semiparametric models with missing covariate data. When covariate data are missing not at random, identification is not guaranteed even under fairly restrictive parametric assumptions, a fact that i...