Journal of the American Statistical Association

最新文章

• Big Data in Omics and Imaging: Integrated Analysis and Causal Inference. AI翻译
  作者：Chen, Oliver Y.
  作者单位：University of Oxford
• Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty AI翻译
  作者：Heaton, Matthew J.; Berrett, Candace; Pugh, Sierra; Evans, Amber; Sloan, Chantel
  作者单位：Brigham Young University; Brigham Young University
  摘要：Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003-2013 in Norfolk, Virginia, USA. For privacy reasons, child home address...
• Optimal Designs for the Two-Dimensional Interference Model AI翻译
  作者：Hedayat, A. S.; Xu, Heng; Zheng, Wei
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Nektar Therapeutics; University of Tennessee System; University of Tennessee Knoxville
  摘要：Recently, there have been some major advances in the theory of optimal designs for interference models when the block is arranged in one-dimensional layout. Relatively speaking, the study for two-dimensional interference model is quite limited partly due to technical difficulties. This article tries to fill this gap. Specifically, we set the tone by characterizing all possible universally optimal designs simultaneously through one linear equations system (LES) with respect to the proportions o...
• Bayesian Graphical Compositional Regression for Microbiome Data AI翻译
  作者：Mao, Jialiang; Chen, Yuhan; Ma, Li
  作者单位：Duke University
  摘要：An important task in microbiome studies is to test the existence of and give characterization to differences in the microbiome composition across groups of samples. Important challenges of this problem include the large within-group heterogeneities among samples and the existence of potential confounding variables that, when ignored, increase the chance of false discoveries and reduce the power for identifying true differences. We propose a probabilistic framework to overcome these issues by c...
• Corrected Bayesian Information Criterion for Stochastic Block Models AI翻译
  作者：Hu, Jianwei; Qin, Hong; Yan, Ting; Zhao, Yunpeng
  作者单位：Central China Normal University; Zhongnan University of Economics & Law; Arizona State University; Arizona State University-Tempe
  摘要：Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models (SBMs) and propose a corrected Bayesian information criterion (CBIC), to determine the number of communities and show that the proposed criterion is consistent under mild conditions as the size of the network and the number of communities go to infinity. The CBIC outperforms those used in Wang and Bickel and Saldana, Yu, and Feng which ...
• Long-Range Dependent Curve Time Series AI翻译
  作者：Li, Degui; Robinson, Peter M.; Shang, Han Lin
  作者单位：University of York - UK; University of London; London School Economics & Political Science; Australian National University
  摘要：We introduce methods and theory for functional or curve time series with long-range dependence. The temporal sum of the curve process is shown to be asymptotically normally distributed, the conditions for this covering a functional version of fractionally integrated autoregressive moving averages. We also construct an estimate of the long-run covariance function, which we use, via functional principal component analysis, in estimating the orthonormal functions spanning the dominant subspace of...
• On High-Dimensional Constrained Maximum Likelihood Inference AI翻译
  作者：Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei
  作者单位：University System of Ohio; Ohio State University; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Inference in a high-dimensional situation may involve regularization of a certain form to treat overparameterization, imposing challenges to inference. The common practice of inference uses either a regularized model, as in inference after model selection, or bias-reduction known as debias. While the first ignores statistical uncertainty inherent in regularization, the second reduces the bias inbred in regularization at the expense of increased variance. In this article, we propose a constrain...
• Handbook of Approximate Bayesian Computation AI翻译
  作者：Franks, Jordan J.
  作者单位：Newcastle University - UK
• Discussion of Prediction, Estimation, and Attribution by Bradley Efron AI翻译
  作者：Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert
  作者单位：Stanford University; Stanford University
  摘要：Professor Efron has presented us with a thought-provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science. While we appreciate many of his arguments, we see more of a continuum between the old and new methodology, and the opportunity for both to improve through their synergy.
• Improved Small-Sample Estimation of Nonlinear Cross-Validated Prediction Metrics AI翻译
  作者：Benkeser, David; Petersen, Maya; van der Laan, Mark J.
  作者单位：Emory University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：When predicting an outcome is the scientific goal, one must decide on a metric by which to evaluate the quality of predictions. We consider the problem of measuring the performance of a prediction algorithm with the same data that were used to train the algorithm. Typical approaches involve bootstrapping or cross-validation. However, we demonstrate that bootstrap-based approaches often fail and standard cross-validation estimators may perform poorly. We provide a general study of cross-validat...