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从固定设计到随机设计回归：偏差方差分解、协方差惩罚与预测误差估计AI 解读

In statistical prediction, classical approaches for model selection and model evaluation based on covariance penalties are still widely used. Most of the literature on this topic is based on what we call the “Fixed-X” assumption, where covariate values are assumed to be nonrandom. By contrast, it is often more reasonable to take a “Random-X” view, where the covariate values are independently drawn for both training and prediction. To study the applicability of covariance…

已撤稿：通过条件粒子滤波器的耦合进行平滑AI 解读

Statement of RetractionWe, the Authors, Editors, and Publishers of the Journal of the American Statistical Association, have retracted the following article:P.E. Jacob, F.Lindsten, T. B. Schön. “Sm...

《病例—对照研究统计方法手册》AI 解读

When I was a PhD student in epidemiology, doing a case-control study of pesticides and non-Hodgkin’s lymphoma, the two best-thumbed books on my shelf were those by Breslow and Day (1980) and Schles...

评论：多重原因的挑战AI 解读

We begin by congratulating Yixin Wang and David Blei for their thought-provoking article that opens up a new research frontier in the field of causal inference. The authors directly tackle the challenging question of how to infer causal effects of many treatments in the presence of unmeasured confounding. We expect their article to have a major impact by further advancing our understanding of this important methodological problem. This commentary has two goals. We first…

多重原因的益处AI 解读

Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal variables and the outcome variables. This assumption is standard but it is also untestable. In this article, we develop the deconfounder, a way to do causal inference with weaker assumptions than the traditional methods require. The deconfounder is designed for problems of multiple causal…

校正计数数据漏报的层级框架AI 解读

Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully…

患者偏好试验的设计、识别与敏感性分析AI 解读

Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’…

小规模实验室验证集下多病原体监测数据的时空建模AI 解读

Many surveillance systems of infectious diseases are syndrome-based, capturing patients by clinical manifestation. Only a fraction of patients, mostly severe cases, undergo laboratory validation to identify the underlying pathogen. Motivated by the need to understand transmission dynamics and associate risk factors of enteroviruses causing the hand, foot and mouth disease (HFMD) in China, we developed a Bayesian spatio-temporal modeling framework for surveillance data of…

古地磁方向数据的缩放 von Mises–Fisher 分布与回归模型AI 解读

We propose a new distribution for analyzing paleomagnetic directional data, that is, a novel transformation of the von Mises–Fisher distribution. The new distribution has ellipse-like symmetry, as does the Kent distribution; however, unlike the Kent distribution the normalizing constant in the new density is easy to compute and estimation of the shape parameters is straightforward. To accommodate outliers, the model also incorporates an additional shape parameter, which…

连接脑区的纤维曲线的非参数贝叶斯模型AI 解读

In studying structural inter-connections in the human brain, it is common to first estimate fiber bundles connecting different regions relying on diffusion MRI. These fiber bundles act as highways for neural activity. Current statistical methods reduce the rich information into an adjacency matrix, with the elements containing a count of fibers or a mean diffusion feature along the fibers. The goal of this article is to avoid discarding the rich geometric information of…

利用贝叶斯自适应样条从大型计算实验推断大气释放特征AI 解读

Devin Francoma*, Bruno Sansób, Vera Bulaevskayac, Donald Lucasd & Matthew Simpsonda Los Alamos National Laboratory, Los Alamos, NM; b University of California Santa Cruz, Santa Cruz, CA; c Climate Corporation, San Francisco, CA; d Lawrence Livermore National Laboratory, Livermore, CAColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to…

BET：基于二进制展开的独立性检验AI 解读

Kai Zhang* Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NCCONTACT Kai Zhang zhangk@email.unc.edu Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.* This article is dedicated to Professor Lawrence D. Brown with the author’s deepest appreciation for his teaching and mentoring.Color versions of one or more of the figures in the article can be found…

工业水力旋流器动态状态估计中的贝叶斯概率数值方法AI 解读

The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this article, we extend Bayesian methods to incorporate statistical models for the error that is incurred in…

空间变量选择及其在弗吉尼亚州莱姆病出现研究中的应用AI 解读

Lyme disease is an infectious disease, that is, caused by a bacterium called Borrelia burgdorferi sensu stricto. In the United States, Lyme disease is one of the most common infectious diseases. The major endemic areas of the disease are New England, Mid-Atlantic, East-North Central, South Atlantic, and West North-Central. Virginia is on the front-line of the disease’s diffusion from the northeast to the south. One of the research objectives for the infectious disease…

大规模网络中的免调参异质性推断AI 解读

Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering meaningful scientific discoveries through the integration of information among subpopulations of interest. In this article, we exploit multiple networks with Gaussian graphs to encode the connectivity patterns of a large number of features on the subpopulations. To uncover the underlying sparsity…

高阈值超越的广义可加模型及其在美国阵风重现水平估计中的应用AI 解读

Generalized additive model (GAM) forms offer a flexible approach to capturing marginal variation. Such forms are used here to represent distributional variation in extreme values and presented in terms of spatio-temporal variation, which is often evident in environmental processes. A two-stage procedure is proposed that identifies extreme values as exceedances of a high threshold, which is defined as a fixed quantile and estimated by quantile regression. Excesses of the…

同伴效应的随机化推断AI 解读

Xinran Lia, Peng Dingb*, Qian Linc, Dawei Yangd & Jun S. Liuaa Department of Statistics, Harvard University, Cambridge, MA; b Department of Statistics, University of California, Berkeley, CA; c Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, P. R. China; d Bureau of Personnel of Chinese Academy of Sciences & School of Education of Peking University, Beijing, P. R. ChinaSupplementary materials for this article are available…

利用校准最优基量化空间输出计算机模型的不确定性AI 解读

The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the “terminal case,” in which the model cannot reproduce observations…

基于Hyvärinen评分的贝叶斯模型比较：计算与一致性AI 解读

The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of out-of-sample predictive scores under the logarithmic scoring rule. However, when some of the candidate models involve vague priors on their parameters, the log-Bayes factor features an arbitrary additive constant that hinders its interpretation. As an alternative, we consider model comparison using the Hyvärinen score. We propose a method to consistently estimate this score…

FarmTest：具有近似错误发现控制的因子调整稳健多重检验AI 解读

Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many…

高维高阶数据的最优稀疏奇异值分解AI 解读

In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD…

时间序列实验与因果目标量：精确随机化检验及交易应用AI 解读

We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization-based p-values for testing causal effects, without imposing stringent assumptions. We further derive a general central limit theorem that can be used to conduct conservative tests and build confidence intervals for causal effects. Finally, we…

动态张量聚类AI 解读

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. There is also a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we propose a new dynamic tensor clustering method that works for a general-order dynamic tensor, and enjoys both strong statistical…

随机比较研究中估计处理效应的稳健 ANCOVA 替代方法AI 解读

In comparing two treatments via a randomized clinical trial, the analysis of covariance (ANCOVA) technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its simple two sample estimation counterpart. Unfortunately, when the ANCOVA model is nonlinear, the resulting estimator is generally not consistent. Recently, various nonparametric alternatives to the ANCOVA, such as the augmentation methods,…