Journal of the American Statistical Association

最新文章

• Accommodating Time-Varying Heterogeneity in Risk Estimation under the Cox Model: A Transfer Learning Approach AI翻译
  作者：Li, Ziyi; Shen, Yu; Ning, Jing
  作者单位：University of Texas System; UTMD Anderson Cancer Center
  摘要：Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to use cancer registry data as a complement to improve the estimation precision of individual risks of death for inflammatory breast cancer (IBC) patients at The University o...
• Metropolis-Hastings via Classification AI翻译
  作者：Kaji, Tetsuya; Rockova, Veronika
  作者单位：University of Chicago
  摘要：This article develops a Bayesian computational platform at the interface between posterior sampling and optimization in models whose marginal likelihoods are difficult to evaluate. Inspired by contrastive learning and Generative Adversarial Networks (GAN), we reframe the likelihood function estimation problem as a classification problem. Pitting a Generator, who simulates fake data, against a Classifier, who tries to distinguish them from the real data, one obtains likelihood (ratio) estimator...
• Fairness-Oriented Learning for Optimal Individualized Treatment Rules AI翻译
  作者：Fang, Ethan X.; Wang, Zhaoran; Wang, Lan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Northwestern University; University of Miami; Duke University
  摘要：There has recently been a surge on the methodological development for optimal individualized treatment rule (ITR) estimation. The standard methods in the literature are designed to maximize the potential average performance (assuming larger outcomes are desirable). A notable drawback of the standard approach, due to heterogeneity in treatment response, is that the estimated optimal ITR may be suboptimal or even detrimental to certain disadvantaged subpopulations. Motivated by the importance of...
• Is a Classification Procedure Good Enough?-A Goodness-of-Fit Assessment Tool for Classification Learning AI翻译
  作者：Zhang, Jiawei; Ding, Jie; Yang, Yuhong
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：In recent years, many nontraditional classification methods, such as random forest, boosting, and neural network, have been widely used in applications. Their performance is typically measured in terms of classification accuracy. While the classification error rate and the like are important, they do not address a fundamental question: Is the classification method underfitted? To our best knowledge, there is no existing method that can assess the goodness of fit of a general classification pro...
• A Kernel Log-Rank Test of Independence for Right-Censored Data AI翻译
  作者：Fernandez, Tamara; Gretton, Arthur; Rindt, David; Sejdinovic, Dino
  作者单位：University of London; University College London; Universidad Adolfo Ibanez; University of Oxford
  摘要：We introduce a general nonparametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to a reproducing kernel Hilbert space (RKHS) of functions; and second, as the norm of the difference of embeddings of certain finite measures into the RKHS, similar to the Hilbert-Sc...
• Functional Time Series Prediction Under Partial Observation of the Future Curve AI翻译
  作者：Jiao, Shuhao; Aue, Alexander; Ombao, Hernando
  作者单位：King Abdullah University of Science & Technology; University of California System; University of California Davis
  摘要：This article tackles one of the most fundamental goals in functional time series analysis which is to provide reliable predictions for future functions. Existing methods for predicting a complete future functional observation use only completely observed trajectories. We develop a new method, called partial functional prediction (PFP), which uses both completely observed trajectories and partial information (available partial data) on the trajectory to be predicted. The PFP method includes an ...
• Conditional Functional Graphical Models AI翻译
  作者：Lee, Kuang-Yao; Ji, Dingjue; Li, Lexin; Constable, Todd; Zhao, Hongyu
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Yale University; University of California System; University of California Berkeley; Yale University
  摘要：Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling. Most existing methods focus on estimating the graph by aggregating samples, but largely ignore the subject-level heterogeneity due to the external variables. In this article, we introd...
• Regression Discontinuity Designs With a Continuous Treatment AI翻译
  作者：Dong, Yingying; Lee, Ying-Ying; Gou, Michael
  作者单位：University of California System; University of California Irvine
  摘要：The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This article establishes identification and robust bias-corrected inference for such RD designs. Causal identification is achieved by using any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). We discuss a double-robust identification approach and propose an estim...
• Modeling Postoperative Mortality in Older Patients by Boosting Discrete-Time Competing Risks Models AI翻译
  作者：Berger, Moritz; Kowark, Ana; Rossaint, Rolf; Coburn, Mark; Schmid, Matthias; POSE Study Grp
  作者单位：University of Bonn; RWTH Aachen University; RWTH Aachen University Hospital; University of Bonn
  摘要：Elderly patients are at a high risk of suffering from postoperative death. Personalized strategies to improve their recovery after intervention are therefore urgently needed. A popular way to analyze postoperative mortality is to develop a prognostic model that incorporates risk factors measured at hospital admission, for example, comorbidities. When building such models, numerous issues must be addressed, including censoring and the presence of competing events (such as discharge from hospita...
• Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA AI翻译
  作者：Hallin, Marc; Hlubinka, Daniel; Hudecova, Sarka
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles; Charles University Prague
  摘要：Extending rank-based inference to a multivariate setting such as multiple-output regression or MANOVA with unspecified d-dimensional error density has remained an open problem for more than half a century. None of the many solutions proposed so far is enjoying the combination of distribution-freeness and efficiency that makes rank-based inference a successful tool in the univariate setting. A concept of center-outward multivariate ranks and signs based on measure transportation ideas has been ...