543期

最新文章

• Discussion of A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models by Chenguang Dai, Buyu Lin, Xin Xing, and Jun S. Liu AI翻译
  作者：Janson, Lucas
  作者单位：Harvard University
• Discussion on: A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models by Dai, Lin, Zing, Liu AI翻译
  作者：Claeskens, Gerda; Jansen, Maarten; Zhou, Jing
  作者单位：KU Leuven; Universite Libre de Bruxelles; Universite Libre de Bruxelles; University of East Anglia; KU Leuven
• Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances AI翻译
  作者：Li, Runze; Xu, Kai; Zhou, Yeqing; Zhu, Liping
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Anhui Normal University; Tongji University; Renmin University of China; Renmin University of China
  摘要：In this article, we test for the effects of high-dimensional covariates on the response. In many applications, different components of covariates usually exhibit various levels of variation, which is ubiquitous in high-dimensional data. To simultaneously accommodate such heteroscedasticity and high dimensionality, we propose a novel test based on an aggregation of the marginal cumulative covariances, requiring no prior information on the specific form of regression models. Our proposed test st...
• Network Structure Learning Under Uncertain Interventions AI翻译
  作者：Castelletti, Federico; Peluso, Stefano
  作者单位：Catholic University of the Sacred Heart; University of Milano-Bicocca
  摘要：Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the network of dependencies among variables, a task which is of primary interest in many fields and specifically in biology. Different DAGs may encode equivalent conditional independence structures, implying limited ability, with observational data, to identify causal relations. In many contexts however, measurements are collected under heterogeneous settings where variables are subject to exogenous interventions. I...
• Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration AI翻译
  作者：Wang, Tianhao; Ratcliffe, Sarah J.; Guo, Wensheng
  作者单位：Rush University; University of Virginia; University of Pennsylvania
  摘要：In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-spe...
• Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference AI翻译
  作者：Wu, Ruijia; Zhang, Linjun; Cai, T. Tony
  作者单位：University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick
  摘要：Sparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed and their theoretical properties are investigated. Both minimax upper and lower bounds are established and the results show that the proposed algorithms are rate-optimal, up to a logarithmic factor. Moreover, a refitting algorithm is proposed to establish as...
• Transport Monte Carlo: High-Accuracy Posterior Approximation via Random Transport AI翻译
  作者：Duan, Leo L.
  作者单位：State University System of Florida; University of Florida
  摘要：In Bayesian applications, there is a huge interest in rapid and accurate estimation of the posterior distribution, particularly for high dimensional or hierarchical models. In this article, we propose to use optimization to solve for a joint distribution (random transport plan) between two random variables, theta from the posterior distribution and beta from the simple multivariate uniform. Specifically, we obtain an approximate estimate of the conditional distribution Pi(beta vertical bar the...
• A Deep Generative Approach to Conditional Sampling AI翻译
  作者：Zhou, Xingyu; Jiao, Yuling; Liu, Jin; Huang, Jian
  作者单位：University of Iowa; Wuhan University; National University of Singapore
  摘要：We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator, so that a random sample from the target conditional distribution can be obtained by transforming a sample drawn from a reference distribution. The conditional generator is estimated nonparametrically with neu...