544期

最新文章

• Coordinatewise Gaussianization: Theories and Applications AI翻译
  作者：Mai, Qing; He, Di; Zou, Hui
  作者单位：State University System of Florida; Florida State University; Nanjing University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：In statistical analysis, researchers often perform coordinatewise Gaussianization such that each variable is marginally normal. The normal score transformation is a method for coordinatewise Gaussianization and is widely used in statistics, econometrics, genetics and other areas. However, few studies exist on the theoretical properties of the normal score transformation, especially in high-dimensional problems where the dimension p diverges with the sample size n. In this article, we show that...
• Rank-Based Greedy Model Averaging for High-Dimensional Survival Data AI翻译
  作者：He, Baihua; Ma, Shuangge; Zhang, Xinyu; Zhu, Li-Xing
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Yale University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Beijing Normal University; Beijing Normal University Zhuhai; Hong Kong Baptist University
  摘要：Model averaging is an effective way to enhance prediction accuracy. However, most previous works focus on low-dimensional settings with completely observed responses. To attain an accurate prediction for the risk effect of survival data with high-dimensional predictors, we propose a novel method: rank-based greedy (RG) model averaging. Specifically, adopting the transformation model with splitting predictors as working models, we doubly use the smooth concordance index function to derive the c...
• Distribution-Free Prediction Sets for Two-Layer Hierarchical Models AI翻译
  作者：Dunn, Robin; Wasserman, Larry; Ramdas, Aaditya
  作者单位：Novartis; Novartis USA; Carnegie Mellon University; Carnegie Mellon University
  摘要：We consider the problem of constructing distribution-free prediction sets for data from two-layer hierarchical distributions. For iid data, prediction sets can be constructed using the method of conformal prediction. The validity of conformal prediction hinges on the exchangeability of the data, which does not hold when groups of observations come from distinct distributions, such as multiple observations on each patient in a medical database. We extend conformal methods to a hierarchical sett...
• Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials AI翻译
  作者：Ye, Ting; Shao, Jun; Yi, Yanyao; Zhao, Qingyuan
  作者单位：University of Washington; University of Washington Seattle; East China Normal University; University of Wisconsin System; University of Wisconsin Madison; Eli Lilly; University of Cambridge
  摘要：In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when modelassisted inference is applied to adjust for covariates under simple or covariate...
• Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models AI翻译
  作者：Bai, Peiliang; Safikhani, Abolfazl; Michailidis, George
  作者单位：State University System of Florida; University of Florida; State University System of Florida; University of Florida; State University System of Florida; University of Florida
  摘要：We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is based on a two-step a...
• Covariate-Assisted Sparse Tensor Completion AI翻译
  作者：Ibriga, Hilda S.; Sun, Will Wei
  作者单位：Purdue University System; Purdue University; Purdue University System; Purdue University
  摘要：We aim to provably complete a sparse and highly missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users' click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on nonmissing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often available. In this ...
• Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization AI翻译
  作者：Cai, Jian-Feng; Li, Jingyang; Xia, Dong
  作者单位：Hong Kong University of Science & Technology
  摘要：We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gra...
• Accommodating Time-Varying Heterogeneity in Risk Estimation under the Cox Model: A Transfer Learning Approach AI翻译
  作者：Li, Ziyi; Shen, Yu; Ning, Jing
  作者单位：University of Texas System; UTMD Anderson Cancer Center
  摘要：Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to use cancer registry data as a complement to improve the estimation precision of individual risks of death for inflammatory breast cancer (IBC) patients at The University o...
• Metropolis-Hastings via Classification AI翻译
  作者：Kaji, Tetsuya; Rockova, Veronika
  作者单位：University of Chicago
  摘要：This article develops a Bayesian computational platform at the interface between posterior sampling and optimization in models whose marginal likelihoods are difficult to evaluate. Inspired by contrastive learning and Generative Adversarial Networks (GAN), we reframe the likelihood function estimation problem as a classification problem. Pitting a Generator, who simulates fake data, against a Classifier, who tries to distinguish them from the real data, one obtains likelihood (ratio) estimator...
• Modeling Postoperative Mortality in Older Patients by Boosting Discrete-Time Competing Risks Models AI翻译
  作者：Berger, Moritz; Kowark, Ana; Rossaint, Rolf; Coburn, Mark; Schmid, Matthias; POSE Study Grp
  作者单位：University of Bonn; RWTH Aachen University; RWTH Aachen University Hospital; University of Bonn
  摘要：Elderly patients are at a high risk of suffering from postoperative death. Personalized strategies to improve their recovery after intervention are therefore urgently needed. A popular way to analyze postoperative mortality is to develop a prognostic model that incorporates risk factors measured at hospital admission, for example, comorbidities. When building such models, numerous issues must be addressed, including censoring and the presence of competing events (such as discharge from hospita...