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作者：Shen, Xiaotong; Huang, Hsin-Cheng
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Academia Sinica - Taiwan
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作者：Feng, Qian; Vuong, Quang; Xu, Haiqing
作者单位：University of Texas System; University of Texas Austin; New York University
摘要：This article estimates individual treatment effects (ITE) and its probability distribution in a triangular model with binary-valued endogenous treatments. Our estimation procedure takes two steps. First, we estimate the counterfactual outcome and hence, the ITE for every observational unit in the sample. Second, we estimate the ITE density function of the whole population. Our estimation method does not suffer from the ill-posed inverse problem associated with inverting a nonlinear functional....
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作者：Xie, Fangzheng; Xu, Yanxun
作者单位：Johns Hopkins University
摘要：We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the ind...
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作者：Wilson, Douglas R.; Ibrahim, Joseph G.; Sun, Wei
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Fred Hutchinson Cancer Center
摘要：The study of gene expression quantitative trait loci (eQTL) is an effective approach to illuminate the functional roles of genetic variants. Computational methods have been developed for eQTL mapping using gene expression data from microarray or RNA-seq technology. Application of these methods for eQTL mapping in tumor tissues is problematic because tumor tissues are composed of both tumor and infiltrating normal cells (e.g., immune cells) and eQTL effects may vary between tumor and infiltrati...
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作者：Argiento, Raffaele; Crennaschi, Andrea; Vannucci, Marina
作者单位：University of Turin; Collegio Carlo Alberto; University of Oslo; University of Oslo; Rice University
摘要：In this article, we propose a Bayesian nonparametric model for clustering grouped data. We adopt a hierarchical approach: at the highest level, each group of data is modeled according to a mixture, where the mixing distributions are conditionally independent normalized completely random measures (NormCRMs) centered on the same base measure, which is itself a NormCRM. The discreteness of the shared base measure implies that the processes at the data level share the same atoms. This desired feat...
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作者：Harman, Radoslav; Filova, Lenka; Richtarik, Peter
作者单位：Comenius University Bratislava; Johannes Kepler University Linz; King Abdullah University of Science & Technology; University of Edinburgh; Moscow Institute of Physics & Technology
摘要：We propose a class of subspace ascent methods for computing optimal approximate designs that covers existing algorithms as well as new and more efficient ones. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to that of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality, which also h...
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作者：Pan, Wenliang; Wang, Xueqin; Zhang, Heping; Zhu, Hongtu; Zhu, Jin
作者单位：Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Yale University; University of Texas System; UTMD Anderson Cancer Center
摘要：Technological advances in science and engineering have led to the routine collection of large and complex data objects, where the dependence structure among those objects is often of great interest. Those complex objects (e.g., different brain subcortical structures) often reside in some Banach spaces, and hence their relationship cannot be well characterized by most of the existing measures of dependence such as correlation coefficients developed in Hilbert spaces. To overcome the limitations...
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作者：Wang, Jingshen; He, Xuming; Xu, Gongjun
作者单位：University of Michigan System; University of Michigan
摘要：This article concerns the potential bias in statistical inference on treatment effects when a large number of covariates are present in a linear or partially linear model. While the estimation bias in an under-fitted model is well understood, we address a lesser-known bias that arises from an over-fitted model. The over-fitting bias can be eliminated through data splitting at the cost of statistical efficiency, and we show that smoothing over random data splits can be pursued to mitigate the e...
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作者：Wu, Peng; Zeng, Donglin; Wang, Yuanjia
作者单位：Columbia University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle c...
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作者：Fan, Yingying; Demirkaya, Emre; Li, Gaorong; Lv, Jinchi
作者单位：University of Southern California; University of Tennessee System; University of Tennessee Knoxville; Beijing University of Technology
摘要：Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this article, we provide theoretical foundations on the power and robustness for the model-X knockoffs procedure introduced recently in Candes, Fan, Janson and Lv in high-dimensional setting when the covariate distribution is characterized by Gaussian graphical model. We establish that under mild regularity conditions, the power o...
