2023

最新文章

• Query-Augmented Active Metric Learning AI翻译
  作者：Deng, Yujia; Yuan, Yubai; Fu, Haoda; Qu, Annie
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Irvine; Eli Lilly
  摘要：In this article, we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, while estimating underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and efficient clustering process. In particular, we augment the queried constraints by generating more pairwise labels to provide additional information in learning a metric to enhance clustering performance...
• A General Framework for Inference on Algorithm-Agnostic Variable Importance AI翻译
  作者：Williamson, Brian D.; Gilbert, Peter B.; Simon, Noah R.; Carone, Marco
  作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response-in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading...
• Evaluating Association Between Two Event Time Observations Subject to informative Censoring AI翻译
  作者：Li, Dongdong; Hu, X. Joan; Wang, Rui
  作者单位：Harvard Pilgrim Health Care; Harvard University; Harvard Medical School; Simon Fraser University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：This article is concerned with evaluating the association between two event times without specifying the joint distribution parametrically. This is particularly challenging when the observations on the event times are subject to informative censoring due to a terminating event such as death. There are few methods suitable for assessing covariate effects on association in this context. We link the joint distribution of the two event times and the informative censoring time using a nested copula...
• Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment AI翻译
  作者：Wang, Bingkai; Susukida, Ryoko; Mojtabai, Ramin; Amin-Esmaeili, Masoumeh; Rosenblum, Michael
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Tehran University of Medical Sciences
  摘要：Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize on the combined precision gains from these two methods, which can lead to wasted resources in terms of sample size and trial duration. We derive consistency and asymptotic normality of model-robust estimators that combine these two methods, and showthat these estimators can lead to substantial gains in precision and p...
• Multivariate Temporal Point Process Regression AI翻译
  作者：Tang, Xiwei; Li, Lexin
  作者单位：University of Virginia; University of California System; University of California Berkeley
  摘要：Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corre...
• Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages AI翻译
  作者：Wilms, Ines; Basu, Sumanta; Bien, Jacob; Matteson, David S.
  作者单位：Maastricht University; Cornell University; University of Southern California
  摘要：The vector autoregressive moving average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive vector autoregressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is simple...
• Semiparametric Efficiency in Convexity Constrained Single-Index Model AI翻译
  作者：Kuchibhotla, Arun K.; Patra, Rohit K.; Sen, Bodhisattva
  作者单位：Carnegie Mellon University; State University System of Florida; University of Florida; Columbia University
  摘要：We consider estimation and inference in a single-index regression model with an unknown convex link function. We introduce a convex and Lipschitz constrained least-square estimator (CLSE) for both the parametric and the nonparametric components given independent and identically distributed observations. We prove the consistency and find the rates of convergence of the CLSE when the errors are assumed to have only q >= 2 moments and are allowed to depend on the covariates. When q >= 5, we estab...
• Honest Confidence Sets for High-Dimensional Regression by Projection and Shrinkage AI翻译
  作者：Zhou, Kun; Li, Ker-Chau; Zhou, Qing
  作者单位：University of California System; University of California Los Angeles; Academia Sinica - Taiwan
  摘要：The issue of honesty in constructing confidence sets arises in nonparametric regression. While optimal rate in nonparametric estimation can be achieved and utilized to construct sharp confidence sets, severe degradation of confidence level often happens after estimating the degree of smoothness. Similarly, for high-dimensional regression, oracle inequalities for sparse estimators could be utilized to construct sharp confidence sets. Yet, the degree of sparsity itself is unknown and needs to be...
• Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis AI翻译
  作者：Zu, Tianhai; Lian, Heng; Green, Brittany; Yu, Yan
  作者单位：University System of Ohio; University of Cincinnati; City University of Hong Kong; University of Louisville
  摘要：Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphism (SNP) at a time. We aim to select among over half a million SNPs along with time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. Taking advantage of rich data fro...
• LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures AI翻译
  作者：Zhang, Zhengwu; Wu, Yuexuan; Xiong, Di; Ibrahim, Joseph G.; Srivastava, Anuj; Zhu, Hongtu
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; State University System of Florida; Florida State University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain's subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcort...