2020

最新文章

• A Bayesian Latent Class Model to Predict Kidney Obstruction in the Absence of Gold Standard AI翻译
  作者：Chang, Changgee; Jang, Jeong Hoon; Manatunga, Amita; Taylor, Andrew T.; Long, Qi
  作者单位：University of Pennsylvania; Emory University; Emory University
  摘要：Kidney obstruction, if untreated in a timely manner, can lead to irreversible loss of renal function. A widely used technology for evaluations of kidneys with suspected obstruction is diuresis renography. However, it is generally very challenging for radiologists who typically interpret renography data in practice to build high level of competency due to the low volume of renography studies and insufficient training. Another challenge is that there is currently no gold standard for detection o...
• Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples AI翻译
  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...
• Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals AI翻译
  作者：Guan, Qian; Reich, Brian J.; Laber, Eric B.; Bandyopadhyay, Dipankar
  作者单位：North Carolina State University; Virginia Commonwealth University
  摘要：Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We...
• Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting AI翻译
  作者：McAlinn, Kenichiro; Aastveit, Knut Are; Nakajima, Jouchi; West, Mike
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Duke University; Norges Bank; BI Norwegian Business School; Bank of Japan
  摘要：We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of multistep macroeconomic forecasting in a monetary policy setting. BPS evaluates?sequentially and adaptively over time?varying forecast biases and facets of miscalibration of individual forecast densities for...
• Studentized Sensitivity Analysis for the Sample Average Treatment Effect in Paired Observational Studies AI翻译
  作者：Fogarty, Colin B.
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：A fundamental limitation of causal inference in observational studies is that perceived evidence for an effect might instead be explained by factors not accounted for in the primary analysis. Methods for assessing the sensitivity of a study's conclusions to unmeasured confounding have been established under the assumption that the treatment effect is constant across all individuals. In the potential presence of unmeasured confounding, it has been argued that certain patterns of effect heteroge...
• Additive Functional Regression for Densities as Responses AI翻译
  作者：Han, Kyunghee; Mueller, Hans-Georg; Park, Byeong U.
  作者单位：University of California System; University of California Davis; Seoul National University (SNU)
  摘要：We propose and investigate additive density regression, a novel additive functional regression model for situations where the responses are random distributions that can be viewed as random densities and the predictors are vectors. Data in the form of samples of densities or distributions are increasingly encountered in statistical analysis and there is a need for flexible regression models that accommodate random densities as responses. Such models are of special interest for multivariate con...
• A Simple Two-Sample Test in High Dimensions Based on L2-Norm AI翻译
  作者：Zhang, Jin-Ting; Guo, Jia; Zhou, Bu; Cheng, Ming-Yen
  作者单位：National University of Singapore; Zhejiang University of Technology; Zhejiang Gongshang University; Hong Kong Baptist University
  摘要：Testing the equality of two means is a fundamental inference problem. For high-dimensional data, the Hotelling's T-2-test either performs poorly or becomes inapplicable. Several modifications have been proposed to address this issue. However, most of them are based on asymptotic normality of the null distributions of their test statistics which inevitably requires strong assumptions on the covariance. We study this problem thoroughly and propose an L-2-norm based test that works under mild con...
• Hierarchical Normalized Completely Random Measures to Cluster Grouped Data AI翻译
  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...
• A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments AI翻译
  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...
• A Tuning-free Robust and Efficient Approach to High-dimensional Regression AI翻译
  作者：Wang, Lan; Peng, Bo; Bradic, Jelena; Li, Runze; Wu, Yunan
  作者单位：University of Miami; Adobe Systems Inc.; University of California System; University of California San Diego; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We introduce a novel approach for high-dimensional regression with theoretical guarantees. The new procedure overcomes the challenge of tuning parameter selection of Lasso and possesses several appealing properties. It uses an easily simulated tuning parameter that automatically adapts to both the unknown random error distribution and the correlation structure of the design matrix. It is robust with substantial efficiency gain for heavy-tailed random errors while maintaining high efficiency fo...