Wodan Ling,Bin Cheng,Ying Wei et al.
Wodan Ling et al.
An extension of quantile regression is proposed to model zero-inflated outcomes, which have become increasingly common in biomedical studies. The method is flexible enough to depict complex and nonlinear associations between the covariates ...
Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data [0.03%]
基于先验知识的超高维变量筛选方法及其在神经影像数据中的应用
Jie He,Jian Kang
Jie He
Variable screening is a powerful and efficient tool for dimension reduction under ultrahigh dimensional settings. However, most existing methods overlook useful prior knowledge in specific applications. In this work, from a Bayesian modelin...
A spline-based nonparametric analysis for interval-censored bivariate survival data [0.03%]
基于样条的间断检測数据非参数分析方法研究
Yuan Wu,Ying Zhang,Junyi Zhou
Yuan Wu
In this manuscript we propose a spline-based sieve nonparametric maximum likelihood estimation method for joint distribution function with bivariate interval-censored data. We study the asymptotic behavior of the proposed estimator by provi...
Leying Guan,Zhou Fan,Robert Tibshirani
Leying Guan
We propose a new method for supervised learning. The hubNet procedure fits a hub-based graphical model to the predictors, to estimate the amount of "connection" that each predictor has with other predictors. This yields a set of predictor w...
Sufficient dimension reduction with simultaneous estimation of effective dimensions for time-to-event data [0.03%]
同时估计有效维度的时间事件数据的充分降维
Ming-Yueh Huang,Kwun Chuen Gary Chan
Ming-Yueh Huang
When there is not enough scientific knowledge to assume a particular regression model, sufficient dimension reduction is a flexible yet parsimonious nonparametric framework to study how covariates are associated with an outcome. We propose ...
Maximum Likelihood Estimation for Cox Proportional Hazards Model with a Change Hyperplane [0.03%]
带有变分超平面的Cox比例危险模型的最大似然估计
Yu Deng,Jianwen Cai,Donglin Zeng
Yu Deng
We propose a Cox proportional hazards model with a change hyperplane to allow the effect of risk factors to differ depending on whether a linear combination of baseline covariates exceeds a threshold. The proposed model is a natural extensi...
STRUCTURED CORRELATION DETECTION WITH APPLICATION TO COLOCALIZATION ANALYSIS IN DUAL-CHANNEL FLUORESCENCE MICROSCOPIC IMAGING [0.03%]
应用于双通道荧光显微图像共定位分析的结构相关检测方法
Shulei Wang,Jianqing Fan,Ginger Pocock et al.
Shulei Wang et al.
Current workflows for colocalization analysis in fluorescence microscopic imaging introduce significant bias in terms of the user's choice of region of interest (ROI). In this work, we introduce an automatic, unbiased structured detection m...
Yin Xia,Lexin Li
Yin Xia
Comparing two population means of network data is of paramount importance in a wide range of scientific applications. Numerous existing network inference solutions focus on global testing of entire networks, without comparing individual net...
Halley Brantley,Montserrat Fuentes,Joseph Guinness et al.
Halley Brantley et al.
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis func...
Causal Proportional Hazards Estimation with a Binary Instrumental Variable [0.03%]
具有二元工具变量的因果比例风险估计
Behzad Kianian,Jung In Kim,Jason P Fine et al.
Behzad Kianian et al.
Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, particularly for structural linear equation models, where simple t...