Journal of the American Statistical Association

最新文章

• Semiparametric Joint Modeling for Survival Analysis With Longitudinal Covariates AI翻译
  作者：Guo, Wensheng; Wang, Tianhao
  作者单位：University of Pennsylvania; Pennsylvania Medicine; Rush University
  摘要：Characterizing the association between survival time and the dynamic patterns of a longitudinal covariate trajectory is of particular interest in many studies. Classical time-dependent survival models focus mainly on the link between the concurrent covariate value and the instantaneous hazard function. Consequently, the conditional survival function is often not properly defined on the whole time range, which causes difficulty in model estimation and interpretation. In this article, we propose...
• Bayesian Image Mediation Analysis AI翻译
  作者：Xu, Yuliang; Johnson, Timothy D.; Heitzeg, Mary; Kang, Jian
  作者单位：University of Chicago; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Mediation analysis aims to separate the indirect effect through mediators from the direct effect of the exposure on the outcome. It is challenging to perform mediation analysis with neuroimaging data which involves high dimensionality, complex spatial correlations, sparse activation patterns and relatively low signal-to-noise ratio. To address these issues, we develop a new spatially varying coefficient structural equation model for Bayesian Image Mediation Analysis (BIMA). We define spatially...
• Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints AI翻译
  作者：Caner, Mehmet; Fan, Qingliang
  作者单位：North Carolina State University; Chinese University of Hong Kong
  摘要：This article explores the statistical properties of forming constrained optimal portfolios within a high-dimensional set of assets. We examine portfolios with tracking error constraints, those with simultaneous tracking error and weight restrictions, and portfolios constrained solely by weight. Tracking error measures portfolio performance against a benchmark (typically an index), while weight constraints determine asset allocation based on regulatory requirements or fund prospectuses. Our app...
• Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy AI翻译
  作者：Zhong, Han; Deng, Xun; Fang, Ethan X.; Yang, Zhuoran; Wang, Zhaoran; Li, Runze
  作者单位：Peking University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Duke University; Yale University; Northwestern University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：While deep reinforcement learning has achieved tremendous successes in various applications, most existing works focus on maximizing the expected value of total return and ignore its inherent stochasticity. Such stochasticity is also known as the aleatoric uncertainty and is closely related to the notion of risk. This work makes the first attempt to study risk-sensitive deep reinforcement learning under the average reward setting with the variance risk criteria. Particularly, we focus on a var...
• Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities AI翻译
  作者：Cao, Jian; Katzfuss, Matthias
  作者单位：University of Houston System; University of Houston; University of Wisconsin System; University of Wisconsin Madison
  摘要：Multivariate normal (MVN) probabilities arise in myriad applications, but they are analytically intractable and need to be evaluated via Monte Carlo-based numerical integration. For the state-of-the-art minimax exponential tilting (MET) method, we show that the complexity of each of its components can be greatly reduced through an integrand parameterization that uses the sparse inverse Cholesky factor produced by the Vecchia approximation, whose approximation error is often negligible relative...
• A Goodness-of-Fit Assessment for General Learning Procedures in High Dimensions AI翻译
  作者：He, Chenxuan; Chen, Canyi; Zhu, Liping
  作者单位：Renmin University of China; University of Michigan System; University of Michigan
  摘要：Black-box learners have demonstrated remarkable success across various fields due to their high predictive accuracy. However, the complexity of their learning procedures poses significant challenges in evaluating whether a given learner has achieved optimal performance on datasets with unknown data-generating mechanisms. We propose a general goodness-of-fit test for assessing different learning procedures involving high-dimensional predictors, encompassing methods from classical linear regress...
• Kernel Density Estimation with Polyspherical Data and its Applications AI翻译
  作者：Garcia-Portugues, Eduardo; Meilan-Vila, Andrea
  作者单位：Universidad Carlos III de Madrid
  摘要：A kernel density estimator for data on the polysphere S(d1)x & ctdot;xS(dr), with r,d(1),& mldr;,d(r)>= 1, is presented in this article. We derive the main asymptotic properties of the estimator, including mean square error, normality, and optimal bandwidths. We address the kernel theory of the estimator beyond the von Mises-Fisher kernel, introducing new kernels that are more efficient and investigating normalizing constants, moments, and sampling methods thereof. Plug-in and cross-validated ...
• Deep Fréchet Regression AI翻译
  作者：Iao, Su I.; Zhou, Yidong; Muller, Hans-Georg
  作者单位：University of California System; University of California Davis
  摘要：Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This article addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the absence of linear struc...
• Network-Based Neighborhood Regression AI翻译
  作者：Zhen, Yaoming; Du, Jin-Hong
  作者单位：University of Toronto; Carnegie Mellon University; Carnegie Mellon University
  摘要：Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This article proposes a novel network-based neighborhood regression framework whose regression func...
• Fast Signal Region Detection With Application to Whole Genome Association Studies AI翻译
  作者：Zhang, Wei; Wang, Fan; Yao, Fang
  作者单位：Peking University; Columbia University
  摘要：Research on the localization of the genetic basis associated with diseases or traits has been widely conducted in the last few decades. Scan methods have been developed for region-based analysis in whole-genome association studies, helping us better understand how genetics influences human diseases or traits, especially when the aggregated effects of multiple causal variants are present. In this paper, we propose a fast and effective algorithm coupling with high-dimensional test for simultaneo...