Journal of the American Statistical Association

最新文章

• Causal Inference in Pharmaceutical Statistics AI翻译
  作者：Buchanan, Ashley L.
  作者单位：University of Rhode Island
• Elastic Shape Analysis of Movement Data AI翻译
  作者：Borgert, J. E.; Hannig, Jan; Tucker, J. Derek; Arbeeva, Liubov; Buck, Ashley N.; Golightly, Yvonne M.; Messier, Stephen P.; Nelson, Amanda E.; Marron, J. S.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; United States Department of Energy (DOE); Sandia National Laboratories; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of Nebraska System; University of Nebraska Medical Center; University of North Carolina; University of North Carolina Chapel Hill; Wake Forest University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Osteoarthritis (OA) is a highly prevalent degenerative joint disease, and the knee is the most commonly affected joint. Biomechanical factors, particularly forces exerted during walking, are often measured in modern studies of knee joint injury and OA, and understanding the relationship among biomechanics, clinical profiles, and OA has high clinical relevance. Biomechanical forces are typically represented as curves over time, but a standard practice in biomechanics research is to summarize th...
• A Minimax Two-Sample Test for Functional Data via Grothendieck's Divergence AI翻译
  作者：Chen, Yan; Lin, Hongmei; Wang, Xueqin; Wen, Canhong
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shanghai University of International Business & Economics; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：Our proposed approach addresses the challenges associated with nonparametric two-sample testing for densely measured functional data. These challenges stem from the high dimensionality of data and the nature of the observation scheme. We introduce a novel metric concept for random functions known as Grothendieck's divergence to overcome these challenges, which satisfies the homogeneity-zero equivalence property. Our approach uses a pre-smoothing technique on densely measured functional data an...
• SOFARI: High-Dimensional Manifold-Based Inference AI翻译
  作者：Zheng, Zemin; Zhou, Xin; Fan, Yingying; Lv, Jinchi
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California
  摘要：Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers. However, conducting precise inference on the latent factor matrices has remained challen...
• Additive Multi-Index Gaussian Process Modeling, with Application to Multi-Physics Surrogate Modeling of the Quark-Gluon Plasma AI翻译
  作者：Li, Kevin; Mak, Simon; Paquet, J. -F; Bass, Steffen A.
  作者单位：Duke University; Vanderbilt University; Vanderbilt University; Duke University
  摘要：The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that, to reconcile experimental observables with theoretical parameters, one requires many simulation runs of a complex physics model over a high-dimensional parameter space. Each run is computationally expensive, requiring thousands of CPU hours, thus limiting physicists to only several hundred runs. Given limited train...
• Design-Based Uncertainty for Quasi-Experiments AI翻译
  作者：Rambachan, Ashesh; Roth, Jonathan
  作者单位：Massachusetts Institute of Technology (MIT); Brown University
  摘要：Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities ...
• Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment AI翻译
  作者：Heng, Siyu; Zhang, Jiawei; Feng, Yang
  作者单位：New York University; New York University; University of Chicago; New York University
  摘要：Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-population models. Despite its advantages, design-based causal inference can still suffer from other issues, among which outcome missingness is a...
• A Practical Interval Estimation Method for Spectral Density Function AI翻译
  作者：Yu, Haihan; Kaiser, Mark S.; Nordman, Daniel J.
  作者单位：University of Rhode Island; Iowa State University
  摘要：The spectral density function can play a key role in time series analysis, where nonparametric interval estimation of the spectral density is a fundamental issue. However, the prevailing pointwise interval methods for spectral densities, including Chi-square approximation and frequency domain bootstrap (FDB), can be misleading in practice, perhaps more so than appreciated, as confidence intervals often exhibit low coverage accuracy as well as high sensitivity to tuning parameters. To provide a...
• Empirical Bayes Estimation via Data Fission AI翻译
  作者：Ignatiadis, Nikolaos; Sun, Dennis L.
  作者单位：University of Chicago; Stanford University
• Scalable Bayesian Inference for Time Series via Divide and Conquer AI翻译
  作者：Ou, Rihui; Astfalck, Lachlan; Sen, Deborshee; Dunson, David
  作者单位：Duke University; University of New South Wales Sydney; University of Western Australia
  摘要：Bayesian computation often scales poorly with increasing data size, motivating developments such as divide-and-conquer approaches for scalable inference. These methods partition the data into subsets, perform parallel inference on each subset, and aggregate the results into a single posterior. Appealing theoretical properties and practical performance have been demonstrated for independent data; however, methods for dependent data remain challenging. Existing methods rely on ad hoc approximati...