Journal of the American Statistical Association

最新文章

• Generalizing Quantile Regression for Counting Processes With Applications to Recurrent Events AI翻译
  作者：Sun, Xiaoyan; Peng, Limin; Huang, Yijian; Lai, HuiChuan J.
  作者单位：Emory University; Rollins School Public Health; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：In survival analysis, quantile regression has become a useful approach to account for covariate effects on the distribution of an event time of interest. In this article, we discuss how quantile regression can be extended to model counting processes and thus lead to a broader regression framework for survival data. We specifically investigate the proposed modeling of counting processes for recurrent events data. We show that the new recurrent events model retains the desirable features of quan...
• Rejoinder AI翻译
  作者：Chen, Guanhua; Zeng, Donglin; Kosorok, Michael R.
  作者单位：Vanderbilt University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times Comment AI翻译
  作者：Guan, Qian; Laber, Eric B.; Reich, Brian J.
  作者单位：North Carolina State University
• Generalized Fiducial Inference: A Review and New Results AI翻译
  作者：Hannig, Jan; Iyer, Hari; Lai, Randy C. S.; Lee, Thomas C. M.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; National Institute of Standards & Technology (NIST) - USA; University of California System; University of California Davis; University of Maine System; University of Maine Orono
  摘要：R. A. Fisher, the father of modern statistics, proposed the idea of fiducial inference during the first half of the 20th century. While his proposal led to interesting methods for quantifying uncertainty, other prominent statisticians of the time did-not accept Fisher's approach as it became apparent that some of Fisher's bold claims about the properties of fiducial distribution did not hold up for multi-parameter problems. Beginning around the year 2000, the authors, and collaborators started...
• Coverage Inducing Priors in Nonstandard Inference Problems AI翻译
  作者：Muller, Ulrich K.; Norets, Andriy
  作者单位：Princeton University; Brown University
  摘要：We consider the construction of set estimators that possess both Bayesian credibility and frequentist coverage properties. We show that under mild regularity conditions there exists a prior distribution that induces (1 - alpha) frequentist coverage of a (1 - alpha) credible set. In contrast to the previous literature, this result does not rely on asymptotic normality or invariance, so it can be applied in nonstandard inference problems. Supplementary materials for this article are available on...
• Simultaneous Sparse Estimation of Canonical Vectors in the p >> N Setting AI翻译
  作者：Gaynanova, Irina; Booth, James G.; Wells, Martin T.
  作者单位：Cornell University
  摘要：This article considers the problem of sparse estimation of canonical vectors in linear discriminant analysis when p >> N. Several methods have been proposed in the literature that estimate one canonical vector in the two-group case. However, G 1 canonical vectors can be considered if the number of groups is G. In the multi-group context, it is common to estimate canonical vectors in a sequential fashion. Moreover, separate prior estimation of the covariance structure is often required. We prop...
• Low SNR in Diffusion MRI Models AI翻译
  作者：Polzehl, Joerg; Tabelow, Karsten
  作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：Noise is a common issue for all magnetic resonance imaging (MRI) techniques such as diffusion MRI and obviously leads to variability of the estimates in any model describing the data. Increasing spatial resolution in MR experiments further diminishes the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from the true value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating proc...
• A Bayesian Approach to Graphical Record Linkage and Deduplication AI翻译
  作者：Steorts, Rebecca C.; Hall, Rob; Fienberg, Stephen E.
  作者单位：Duke University; Duke University; Carnegie Mellon University; Carnegie Mellon University
  摘要：We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable peo...
• Appreciating Statistics AI翻译
  作者：Utts, Jessica
  作者单位：University of California System; University of California Irvine
• A Subsampled Double Bootstrap for Massive Data AI翻译
  作者：Sengupta, Srijan; Volgushev, Stanislav; Shao, Xiaofeng
  作者单位：Virginia Polytechnic Institute & State University; University of Toronto; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets that are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently, Kleiner and co-authors, proposed a method called BLB (bag of little bootstraps) for massive data, which is more computationally scalable with little sacrifice of statistical ...