Journal of the American Statistical Association

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...
• A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions AI翻译
  作者：Molstad, Aaron J.; Rothman, Adam J.
  作者单位：State University System of Florida; University of Florida; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We propose a penalized likelihood method to fit the bivariate categorical response regression model. Our method allows practitioners to estimate which predictors are irrelevant, which predictors only affect the marginal distributions of the bivariate response, and which predictors affect both the marginal distributions and log odds ratios. To compute our estimator, we propose an efficient algorithm which we extend to settings where some subjects have only one response variable measured, that i...
• Structure-Adaptive Sequential Testing for Online False Discovery Rate Control AI翻译
  作者：Gang, Bowen; Sun, Wenguang; Wang, Weinan
  作者单位：Fudan University; University of Southern California
  摘要：Consider the online testing of a stream of hypotheses where a real-time decision must be made before the next data point arrives. The error rate is required to be controlled at all decision points. Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision-making process may come to a halt when the total error budget, or alpha-wealth, is exhausted. This work develops a new class of struc...
• 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...
• Bagged Filters for Partially Observed Interacting Systems AI翻译
  作者：Ionides, Edward L.; Asfaw, Kidus; Park, Joonha; King, Aaron A.
  作者单位：University of Michigan System; University of Michigan; University of Kansas; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Bagging (i.e., bootstrap aggregating) involves combining an ensemble of bootstrap estimators. We consider bagging for inference from noisy or incomplete measurements on a collection of interacting stochastic dynamic systems. Each system is called a unit, and each unit is associated with a spatial location. A motivating example arises in epidemiology, where each unit is a city: the majority of transmission occurs within a city, with smaller yet epidemiologically important interactions arising f...
• Wasserstein Regression AI翻译
  作者：Chen, Yaqing; Lin, Zhenhua; Muller, Hans-Georg
  作者单位：University of California System; University of California Davis; National University of Singapore
  摘要：The analysis of samples of random objects that do not lie in a vector space is gaining increasing attention in statistics. An important class of such object data is univariate probability measures defined on the real line. Adopting the Wasserstein metric, we develop a class of regression models for such data, where random distributions serve as predictors and the responses are either also distributions or scalars. To define this regression model, we use the geometry of tangent bundles of the s...
• RaSE: A Variable Screening Framework via Random Subspace Ensembles AI翻译
  作者：Tian, Ye; Feng, Yang
  作者单位：Columbia University; New York University
  摘要：Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the predictors according to their individual contributions to the response. As a result, variables that are marginally independent but jointly dependent with the response could be missed. In this work, we propose a new framework for variable screening, random subspace ensemble (RaSE), which works by evaluating the quali...
• 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...