546期

最新文章

• Addressing Multiple Detection Limits with Semiparametric Cumulative Probability Models AI翻译
  作者：Tian, Yuqi; Li, Chun; Tu, Shengxin; James, Nathan T.; Harrell, FrankE.; Shepherd, BryanE.
  作者单位：Vanderbilt University; University of Southern California
  摘要：Detection limits (DLs), where a variable cannot be measured outside of a certain range, are common in research. DLs may vary across study sites or over time. Most approaches to handling DLs in response variables implicitly make strong parametric assumptions on the distribution of data outside DLs. We propose a new approach to deal with multiple DLs based on a widely used ordinal regression model, the cumulative probability model (CPM). The CPM is a rank-based, semiparametric linear transformat...
• Efficient Multimodal Sampling via Tempered Distribution Flow AI翻译
  作者：Qiu, Yixuan; Wang, Xiao
  作者单位：Shanghai University of Finance & Economics; Purdue University System; Purdue University
  摘要：Sampling from high-dimensional distributions is a fundamental problem in statistical research and practice. However, great challenges emerge when the target density function is unnormalized and contains isolated modes. We tackle this difficulty by fitting an invertible transformation mapping, called a transport map, between a reference probability measure and the target distribution, so that sampling from the target distribution can be achieved by pushing forward a reference sample through the...
• Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer AI翻译
  作者：Li, Sai; Zhang, Linjun; Cai, T. Tony; Li, Hongzhe
  作者单位：Renmin University of China; Rutgers University System; Rutgers University New Brunswick; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：Transfer learning provides a powerful tool for incorporating data from related studies into a target study of interest. In epidemiology and medical studies, the classification of a target disease could borrow information across other related diseases and populations. In this work, we consider transfer learning for high-dimensional Generalized Linear Models (GLMs). A novel algorithm, TransHDGLM, that integrates data from the target study and the source studies is proposed. Minimax rate of conve...
• A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sum AI翻译
  作者：Hu, Xiaoyu; Lei, Jing
  作者单位：Peking University; Carnegie Mellon University
  摘要：We consider the problem of testing the equality of conditional distributions of a response variable given a vector of covariates between two populations. Such a hypothesis testing problem can be motivated from various machine learning and statistical inference scenarios, including transfer learning and causal predictive inference. We develop a nonparametric test procedure inspired from the conformal prediction framework. The construction of our test statistic combines recent developments in co...
• Two-Way Truncated Linear Regression Models with Extremely Thresholding Penalization AI翻译
  作者：Teng, Hao Yang; Zhang, Zhengjun
  作者单位：Arkansas State University; University of Wisconsin System; University of Wisconsin Madison
  摘要：This article introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar to the tree-based ...
• Partnering With Authors to Enhance Reproducibility at JASA AI翻译
  作者：Wrobel, Julia; Hector, Emily C.; Crawford, Lorin; Mcgowan, Lucy D'Agostino; da Silva, Natalia; Goldsmith, Jeff; Hicks, Stephanie; Kane, Michael; Lee, Youjin; Mayrink, Vinicius; Paciorek, Christopher J.; Usher, Therri; Wolfson, Julian
  作者单位：Emory University; North Carolina State University; Microsoft; Brown University; Wake Forest University; Universidad de la Republica, Uruguay; Columbia University; Johns Hopkins University; Johns Hopkins University; Yale University; Brown University; Universidade Federal de Minas Gerais; University of California System; University of California Berkeley; US Food & Drug Administration (FDA); University of Minnesota System; University of Minnesota Twin Cities
• Statistical Inferences for Complex Dependence of Multimodal Imaging Data AI翻译
  作者：Chang, Jinyuan; He, Jing; Kang, Jian; Wu, Mingcong
  作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Michigan System; University of Michigan
  摘要：Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this article, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data. Motivated by the analysis of multi-task fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence am...
• Crowdsourcing Utilizing Subgroup Structure of Latent Factor Modeling AI翻译
  作者：Xu, Qi; Yuan, Yubai; Wang, Junhui; Qu, Annie
  作者单位：University of California System; University of California Irvine; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Chinese University of Hong Kong; University of California System; University of California Irvine
  摘要：Crowdsourcing has emerged as an alternative solution for collecting large scale labels. However, the majority of recruited workers are not domain experts, so their contributed labels could be noisy. In this article, we propose a two-stage model to predict the true labels for multicategory classification tasks in crowdsourcing. In the first stage, we fit the observed labels with a latent factor model and incorporate subgroup structures for both tasks and workers through a multi-centroid groupin...
• Survival Mixed Membership Blockmodel AI翻译
  作者：Song, Fangda; Chu, Jing; Ma, Shuangge; Wei, Yingying
  作者单位：The Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong; Yale University
  摘要：Whenever we send a message via a channel such as E-mail, Facebook, WhatsApp, WeChat, or LinkedIn, we care about the response rate-the probability that our message will receive a response-and the response time-how long it will take to receive a reply. Recent studies have made considerable efforts to model the sending behaviors of messages in social networks with point processes. However, statistical research on modeling response rates and response times on social networks is still lacking. Comp...
• Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment AI翻译
  作者：Michael, Haben; Cui, Yifan; Lorch, Scott A.; Tchetgen, Eric Tchetgen J.
  作者单位：University of Massachusetts System; University of Massachusetts Amherst; Zhejiang University; University of Pennsylvania; University of Pennsylvania
  摘要：Robins introduced Marginal Structural Models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identification of MSM parameters is established under a Sequential Randomization Assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We consider sufficient conditions for identification of the parameters of a subclass, Margina...