Journal of the American Statistical Association

最新文章

• Data Science and Predictive Analytics, 2nd ed. AI翻译
  作者：Qiu, Xing
  作者单位：University of Rochester
• Recommender Systems: A Review AI翻译
  作者：LeBlanc, Patrick M.; Banks, David; Fu, Linhui; Li, Mingyan; Tang, Zhengyu; Wu, Qiuyi
  作者单位：Duke University; University of North Carolina; University of North Carolina Greensboro; University of Rochester
  摘要：Recommender systems are the engine of online advertising. Not only do they suggest movies, music, or romantic partners, but they also are used to select which advertisements to show to users. This paper reviews the basics of recommender system methodology and then looks at the emerging arena of active recommender systems.
• Copula Based Cox Proportional Hazards Models for Dependent Censoring AI翻译
  作者：Deresa, Negera Wakgari; Keilegom, Ingrid Van
  作者单位：KU Leuven
  摘要：Most existing copula models for dependent censoring in the literature assume that the parameter defining the copula is known. However, prior knowledge on this dependence parameter is often unavailable. In this article we propose a novel model under which the copula parameter does not need to be known. The model is based on a parametric copula model for the relation between the survival time (T) and the censoring time (C), whereas the marginal distributions of T and C follow a semiparametric Co...
• Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding AI翻译
  作者：Qi, Zhengling; Miao, Rui; Zhang, Xiaoke
  作者单位：George Washington University; University of California System; University of California Irvine; George Washington University
  摘要：Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish...
• Robust Inference and Modeling of Mean and Dispersion for Generalized Linear Models AI翻译
  作者：Ponnet, Jolien; Segaert, Pieter; Van Aelst, Stefan; Verdonck, Tim
  作者单位：KU Leuven; University of Antwerp; University of Antwerp
  摘要：Generalized Linear Models (GLMs) are a popular class of regression models when the responses follow a distribution in the exponential family. In real data the variability often deviates from the relation imposed by the exponential family distribution, which results in over- or underdispersion. Dispersion effects may even vary in the data. Such datasets do not follow the traditional GLM distributional assumptions, leading to unreliable inference. Therefore, the family of double exponential dist...
• Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning AI翻译
  作者：Zhou, Wenzhuo; Zhu, Ruoqing; Qu, Annie
  作者单位：University of California System; University of California Irvine; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. However, the practical use of mHealth technology raises unique challenges to existing methodologies on learning an optimal dynamic treatment regime. Many mHealth applications involve decision-making with large numbers of intervention options and under an infinite time horizon setting where the number of decision stages diver...
• An Empirical Bares Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices AI翻译
  作者：Yang, Chun-Hao; Doss, Hani; Vemuri, Baba C.
  作者单位：National Taiwan University; State University System of Florida; University of Florida; State University System of Florida; University of Florida
  摘要：The James-Stein estimator is an estimator of the multivariate normal mean and dominates likelihood estimator (MLE) under squared error loss. The original work inspired great interest in developing shrinkage estimators for a variety of problems. Nonetheless, research on shrinkage estimation for manifold- valued data is scarce. In this article, we propose shrinkage estimators for the parameters of the Log-Normal distribution defined on the manifold of N x N symmetric positive-definite matrices. ...
• Coefficient Shape Alignment in Multiple Functional Linear Regression AI翻译
  作者：Jiao, Shuhao; Chan, Ngai-Hang
  作者单位：City University of Hong Kong
  摘要：In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the same group, offering a parsimonious approach to modeling multivariate functional data. In this article, a novel grouped multiple functional regression model with a new regularization approach termed coefficient shape alignment is developed to tackle functional covariates homogeneity. The modeling procedure includes tw...
• Functional Integrative Bayesian Analysis of High-Dimensional Multiplatform Clinicogenomic Data AI翻译
  作者：Bhattacharyya, Rupam; Henderson, Nicholas C.; Baladandayuthapani, Veerabhadran
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-nov...
• An Efficient Coalescent Model for Heterochronously Sampled Molecular Data AI翻译
  作者：Cappello, Lorenzo; Veber, Amandine; Palacios, Julia A.
  作者单位：Pompeu Fabra University; Barcelona School of Economics; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; Stanford University
  摘要：Molecular sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary parameters. However, it is well understood that inference under this model does not scale well with sample size. Here, we build on recent work based on a lower resolution coalescent process, the Tajima coalescent, to model longitudinal samples. While the Kingman ...