Journal of the American Statistical Association

最新文章

• Residual Importance Weighted Transfer Learning for High-dimensional Linear Regression AI翻译
  作者：Zhao, Junlong; Zheng, Shengbin; Leng, Chenlei
  作者单位：Beijing Normal University; Hong Kong Polytechnic University
  摘要：Transfer learning is an emerging paradigm for leveraging multiple sources to improve the statistical inference on a single target. In this article, we propose a novel approach named residual importance weighted transfer learning (RIW-TL) for high-dimensional linear models built on penalized likelihood. Compared to existing methods such as Trans-Lasso that selects sources in an (approximately) all-in-or-all-out manner, RIW-TL includes samples via importance weighting and thus may permit more ef...
• Bayesian Phase 1-2 Designs with Adaptive Rules for Staggering Patient Entry AI翻译
  作者：Wang, Shuqi; Thall, Peter F.; Yuan, Ying; Liu, Suyu
  作者单位：University of Texas System; UTMD Anderson Cancer Center
  摘要：For first-in-human dose-finding trials, to protect patient safety, regulatory agencies may enforce strict within-cohort staggering rules that require delaying treatment of each patient in the first cohort at an untried dose until dose-limiting toxicities (DLTs) of all previously treated patients have been evaluated. Consequently, many new patients may face therapy delays, which reduces their probability of achieving a response due to disease progression, or be treated off-protocol, which may s...
• Bayesian Optimization for Branching and Nested Hyperparameters in Deep Learning AI翻译
  作者：Zhang, Jiazhao; Lin, Chung-Ching; Hung, Ying
  作者单位：Rutgers University System; Rutgers University New Brunswick; Microsoft
  摘要：Hyperparameter optimization plays a crucial role in the success of neural networks as hyperparameters directly control the behavior and performance of the training algorithms. To obtain efficient tuning, Bayesian optimization based on Gaussian process is widely used. Despite numerous applications in deep learning, the existing methods rely on a convenient but restrictive assumption that the tuning parameters are independent of each other. However, tuning parameters with conditional dependence ...
• Network Regression and Supervised Centrality Estimation AI翻译
  作者：Cai, Junhui; Yang, Dan; Chen, Ran; Shen, Haipeng; Zhao, Linda; Zhu, Wu
  作者单位：University of Notre Dame; University of Hong Kong; Washington University (WUSTL); University of Pennsylvania; Tsinghua University
  摘要：The centrality in a network is often used to measure nodes' importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent network regression a...
• Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent AI翻译
  作者：Chang, Xiangyu; Chen, Xi; Lai, Zehua; Li, He; Liu, Zhihong; Zhang, Yichen
  作者单位：Xi'an Jiaotong University; New York University; University of Texas System; University of Texas Austin; Purdue University System; Purdue University
  摘要：With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for an online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes of the stochastic ...
• Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm AI翻译
  作者：Xia, Xintao; Zhang, Linjun; Cai, Zhanrui
  作者单位：Iowa State University; Rutgers University System; Rutgers University New Brunswick; University of Hong Kong
  摘要：Privacy preservation has become a critical concern in high-dimensional data analysis due to the growing prevalence of data-driven applications. Since its proposal, sliced inverse regression has emerged as a widely used statistical technique to reduce the dimensionality of covariates while maintaining sufficient statistical information. In this used, we propose optimally differentially private algorithms specifically designed to address privacy concerns in the context of sufficient dimension re...
• A Bayesian Nonparametric Approach to Mediation and Spillover Effects with Multiple Mediators in Cluster-Randomized Trials AI翻译
  作者：Ohnishi, Yuki; Li, Fan
  作者单位：Yale University; Yale University
  摘要：Cluster randomized trials (CRTs) with multiple unstructured mediators present significant methodological challenges for causal inference due to within-cluster correlation, interference among units, and the complexity introduced by multiple mediators. Existing causal mediation methods often fall short in simultaneously addressing these complexities, particularly in disentangling mediator-specific effects under interference that are central to studying complex mechanisms. To address this gap, we...
• High-Dimensional Covariance Regression with Application to Co-Expression QTL Detection AI翻译
  作者：Kim, Rakheon; Zhang, Jingfei
  作者单位：Baylor University; Emory University
  摘要：While covariance matrices have been widely studied in many scientific fields, relatively limited progress has been made on estimating conditional covariances that permits a large covariance matrix to vary with high-dimensional subject-level covariates. In this article, we present a new sparse covariance regression framework that models the covariance matrix as a function of subject-level covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can be used ...
• Modeling Tree Survival for Investigating Climate Change Effects AI翻译
  作者：Augustin, Nicole; Albrecht, Axel; Anaya-Izquierdo, Karim; Davis, Alice; Meining, Stefan; Puhlmann, Heike; Wood, Simon
  作者单位：University of Edinburgh
  摘要：Using German forest health monitoring data we investigate the main drivers leading to tree mortality and the association between defoliation and mortality; in particular (a) whether defoliation is a proxy for other covariates (climate, soil, water budget); (b) whether defoliation is a tree response that mitigates the effects of climate change and (c) whether there is a threshold of defoliation which could be used as an early warning sign for irreversible damage. Results show that environmental...
• Identifying Genetic Variants for Brain Connectivity Using Ball Covariance Ranking and Aggregation AI翻译
  作者：Dai, Wei; Zhang, Heping
  作者单位：Yale University
  摘要：Understanding the genetic architecture of brain functions is essential to clarify the biological etiologies of behavioral and psychiatric disorders. Functional connectivity, representing pairwise correlations of neural activities between brain regions, is moderately heritable. Current methods to identify single nucleotide polymorphisms (SNPs) linked to functional connectivity either neglect the complex structure of functional connectivity or fail to control false discoveries. Therefore, we pro...