Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference [0.03%]
具有聚类干扰的高效非参数估计随机政策效应
Chanhwa Lee,Donglin Zeng,Michael G Hudgens
Chanhwa Lee
Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individua...
A New Strategy for Evaluating the Impact of Epidemiologic Risk Factors for Cancer With Application to Melanoma [0.03%]
一种评价流行病学致癌因素影响的新策略及其在黑色素瘤研究中的应用
Colin B Begg,Jaya M Satagopan,Marianne Berwick
Colin B Begg
A new stochastic framework is proposed for evaluating the individual and collective impact of cancer risk factors, and is applied to data on the incidence of melanoma. It is demonstrated that the standardized incidence ratio of second prima...
Ameer Dharamshi,Anna Neufeld,Keshav Motwani et al.
Ameer Dharamshi et al.
Our goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural expo...
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features [0.03%]
具有高维特征的双重稳健增强模型准确性迁移推理
Doudou Zhou,Molei Liu,Mengyan Li et al.
Doudou Zhou et al.
Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on...
Sharp-SSL: Selective High-Dimensional Axis-Aligned Random Projections for Semi-Supervised Learning [0.03%]
Sharp-SSL：半监督学习中用于选择性的高维轴对齐随机投影方法
Tengyao Wang,Edgar Dobriban,Milana Gataric et al.
Tengyao Wang et al.
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data. Our primary goal is t...
Wentao Zhan,Abhirup Datta
Wentao Zhan
Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a Gaussian process covariance model, encoding the spatial dependence. While nonlinear mac...
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects [0.03%]
基于排名加权平均处理效应的治疗优先规则评估
Steve Yadlowsky,Scott Fleming,Nigam Shah et al.
Steve Yadlowsky et al.
There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-weighted average treatment effect (RATE) metr...
Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data [0.03%]
基于bulk表达数据的细胞类型比例的统计推断
Biao Cai,Emma Jingfei Zhang,Hongyu Li et al.
Biao Cai et al.
There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many ...
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning [0.03%]
高维半监督学习的最优和安全估计方法研究
Siyi Deng,Yang Ning,Jiwei Zhao et al.
Siyi Deng et al.
We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of ...