James Robins,Eric Tchetgen Tchetgen,Lingling Li et al.
James Robins et al.
We consider the minimax rate of testing (or estimation) of non-linear functionals defined on semiparametric models. Existing methods appear not capable of determining a lower bound on the minimax rate of testing (or estimation) for certain ...
Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks [0.03%]
基于条件排名的Plackett-Luce模型的可扩展贝叶斯非参数回归方法
Tristan Gray-Davies,Chris C Holmes,François Caron
Tristan Gray-Davies
We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ∈ ℝ. The model is parametrized in terms of marginal distributions for Y and X and a regression function which tunes the stochas...
Tree based weighted learning for estimating individualized treatment rules with censored data [0.03%]
处理审查数据的个体化治疗决策树基权重学习方法研究
Yifan Cui,Ruoqing Zhu,Michael Kosorok
Yifan Cui
Estimating individualized treatment rules is a central task for personalized medicine. [23] and [22] proposed outcome weighted learning to estimate individualized treatment rules directly through maximizing the expected outcome without mode...
Shujie Ma,Heng Lian,Hua Liang et al.
Shujie Ma et al.
While popular, single index models and additive models have potential limitations, a fact that leads us to propose SiAM, a novel hybrid combination of these two models. We first address model identifiability under general assumptions. The r...
Designing penalty functions in high dimensional problems: The role of tuning parameters [0.03%]
高维问题中的罚函数设计：调节参数的作用
Ting-Huei Chen,Wei Sun,Jason P Fine
Ting-Huei Chen
Various forms of penalty functions have been developed for regularized estimation and variable selection. Screening approaches are often used to reduce the number of covariate before penalized estimation. However, in certain problems, the n...
Estimation and inference of error-prone covariate effect in the presence of confounding variables [0.03%]
混杂变量存在的条件下估计和推断有偏预测因子的影响
Jianxuan Liu,Yanyuan Ma,Liping Zhu et al.
Jianxuan Liu et al.
We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the mo...
Chen Gao,Yunzhang Zhu,Xiaotong Shen et al.
Chen Gao et al.
We aim to estimate multiple networks in the presence of sample heterogeneity, where the independent samples (i.e. observations) may come from different and unknown populations or distributions. Specifically, we consider penalized estimation...
Semiparametric Single-Index Model for Estimating Optimal Individualized Treatment Strategy [0.03%]
半参数单指标模型在估计个体化治疗策略中的应用
Rui Song,Shikai Luo,Donglin Zeng et al.
Rui Song et al.
Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneo...
Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process [0.03%]
假设最少的相互激励多元霍克斯过程筛选方法
Shizhe Chen,Daniela Witten,Ali Shojaie
Shizhe Chen
We consider the task of learning the structure of the graph underlying a mutually-exciting multivariate Hawkes process in the high-dimensional setting. We propose a simple and computationally inexpensive edge screening approach. Under a sub...
Robust learning for optimal treatment decision with NP-dimensionality [0.03%]
处理决策的稳健学习方法及其在NP维情况下的应用
Chengchun Shi,Rui Song,Wenbin Lu
Chengchun Shi
In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the...