Feature-weighted elastic net: using "features of features" for better prediction [0.03%]
特征加权弹性网：“特征的特征”在预测中的应用
J Kenneth Tay,Nima Aghaeepour,Trevor Hastie et al.
J Kenneth Tay et al.
In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we ca...
SEMIPARAMETRIC DOSE FINDING METHODS FOR PARTIALLY ORDERED DRUG COMBINATIONS [0.03%]
部分有序药物组合的半参数剂量寻找方法
Matthieu Clertant,Nolan A Wages,John O&#x;Quigley
Matthieu Clertant
We investigate a statistical framework for Phase I clinical trials that test the safety of two or more agents in combination. For such studies, the traditional assumption of a simple monotonic relation between dose and the probability of an...
Robust inference of conditional average treatment effects using dimension reduction [0.03%]
使用降维稳健推断条件平均处理效果
Ming-Yueh Huang,Shu Yang
Ming-Yueh Huang
Personalized treatment aims at tailoring treatments to individual characteristics. An important step is to understand how a treatment effect varies across individual characteristics, known as the conditional average treatment effect (CATE)....
Yeonjoo Park,Xiaohui Chen,Douglas G Simpson
Yeonjoo Park
Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitat...
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...