Journal of the American Statistical Association

最新文章

• Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data AI翻译
  作者：Bacro, Jean-Noel; Gaetan, Carlo; Opitz, Thomas; Toulemonde, Gwladys
  作者单位：Universite de Montpellier; Centre National de la Recherche Scientifique (CNRS); Universita Ca Foscari Venezia; INRAE; Universite de Montpellier; Centre National de la Recherche Scientifique (CNRS); Inria
  摘要：The statistical modeling of space-time extremes in environmental applications is key to understanding complex dependence structures in original event data and to generating realistic scenarios for impact models. In this context of high-dimensional data, we propose a novel hierarchical model for high threshold exceedances defined over continuous space and time by embedding a space-time Gamma process convolution for the rate of an exponential variable, leading to asymptotic independence in space...
• Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses AI翻译
  作者：Mathur, Maya B.; VanderWeele, Tyler J.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Stanford University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Random-effects meta-analyses of observational studies can produce biased estimates if the synthesized studies are subject to unmeasured confounding. We propose sensitivity analyses quantifying the extent to which unmeasured confounding of specified magnitude could reduce to below a certain threshold the proportion of true effect sizes that are scientifically meaningful. We also develop converse methods to estimate the strength of confounding capable of reducing the proportion of scientifically...
• Nonparametric Imputation by Data Depth AI翻译
  作者：Mozharovskyi, Pavlo; Josse, Julie; Husson, Francois
  作者单位：Universite Paris Saclay; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris; Institut Polytechnique de Paris; Ecole Polytechnique; Centre National de la Recherche Scientifique (CNRS); Universite de Rennes; Institut Agro; Institut Agro Rennes-Angers
  摘要：We present single imputation method for missing values which borrows the idea of data depth-a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly defined statistical depth function. For each single iteration, imputation reverts to optimization of quadratic, linear, or quasiconcave functions that...
• Discussion of the Paper Prediction, Estimation, and Attribution by B. Efron AI翻译
  作者：Candes, Emmanuel; Sabatti, Chiara
  作者单位：Stanford University; Stanford University; Stanford University
• Discussion of Paper by Brad Efron AI翻译
  作者：Cox, D. R.
  作者单位：University of Oxford
• Regression Analysis of Doubly Truncated Data AI翻译
  作者：Ying, Zhiliang; Yu, Wen; Zhao, Ziqiang; Zheng, Ming
  作者单位：Columbia University; Fudan University; Novartis
  摘要：Doubly truncated data are found in astronomy, econometrics, and survival analysis literature. They arise when each observation is confined to an interval, that is, only those which fall within their respective intervals are observed along with the intervals. Unlike the one-sided truncation that can be handled by counting process-based approach, doubly truncated data are much more difficult to handle. In their analysis of an astronomical dataset, Efron and Petrosian proposed some nonparametric ...
• Nonparametric Inference for Copulas and Measures of Dependence Under Length-Biased Sampling and Informative Censoring AI翻译
  作者：Rabhi, Yassir; Bouezmarni, Taoufik
  作者单位：State University of New York (SUNY) System; SUNY Cortland; University of Sherbrooke
  摘要：Length-biased data are often encountered in cross-sectional surveys and prevalent-cohort studies on disease durations. Under length-biased sampling subjects with longer disease durations have greater chance to be observed. As a result, covariate values linked to the longer survivors are favored by the sampling mechanism. When the sampled durations are also subject to right censoring, the censoring is informative. Modeling dependence structure without adjusting for these issues leads to biased ...
• A Geometric Variational Approach to Bayesian Inference AI翻译
  作者：Saha, Abhijoy; Bharath, Karthik; Kurtek, Sebastian
  作者单位：University System of Ohio; Ohio State University; University of Nottingham
  摘要：We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in , and the Fisher-Rao metric reduces to the standard metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variati...
• Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression AI翻译
  作者：Zhao, Hui; Wu, Qiwei; Li, Gang; Sun, Jianguo
  作者单位：Zhongnan University of Economics & Law; University of Missouri System; University of Missouri Columbia; University of California System; University of California Los Angeles
  摘要：The simultaneous estimation and variable selection for Cox model has been discussed by several authors when one observes right-censored failure time data. However, there does not seem to exist an established procedure for interval-censored data, a more general and complex type of failure time data, except two parametric procedures. To address this, we propose a broken adaptive ridge (BAR) regression procedure that combines the strengths of the quadratic regularization and the adaptive weighted...
• The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach AI翻译
  作者：Cook, Richard J.
  作者单位：University of Waterloo