Journal of the American Statistical Association

最新文章

• Nonparametric Estimation of Galaxy Cluster Emissivity and Detection of Point Sources in Astrophysics With Two Lasso Penalties AI翻译
  作者：Diaz-Rodriguez, Jairo; Eckert, Dominique; Monajemi, Hatef; Paltani, Stephane; Sardy, Sylvain
  作者单位：Universidad del Norte Colombia; University of Geneva; Stanford University; University of Geneva
  摘要：Astrophysicists are interested in recovering the three-dimensional gas emissivity of a galaxy cluster from a two-dimensional telescope image. Blurring and point sources make this inverse problem harder to solve. The conventional approach requires in a first step to identify and mask the point sources. Instead we model all astrophysical components in a single Poisson generalized linear model. To enforce sparsity on the parameters, maximum likelihood estimation is regularized with twopenalties w...
• Causal Inference With Interference and Noncompliance in Two-Stage Randomized Experiments AI翻译
  作者：Imai, Kosuke; Jiang, Zhichao; Malaniam, Anup
  作者单位：Harvard University; University of Massachusetts System; University of Massachusetts Amherst; University of Chicago; National Bureau of Economic Research
  摘要：In many social science experiments, subjects often interact with each other and as a result one unit's treatment influences the outcome of another unit. Over the last decade, a significant progress has been made toward causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed perfect compliance...
• Sparse Learning and Structure Identification for Ultrahigh-Dimensional Image-on-Scalar Regression AI翻译
  作者：Li, Xinyi; Wang, Li; Wang, Huixia Judy
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Iowa State University; George Washington University
  摘要：This article considers high-dimensional image-on-scalar regression, where the spatial heterogeneity of covariate effects on imaging responses is investigated via a flexible partially linear spatially varying coefficient model. To tackle the challenges of spatial smoothing over the imaging response's complex domain consisting of regions of interest, we approximate the spatially varying coefficient functions via bivariate spline functions over triangulation. We first study estimation when the ac...
• Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models AI翻译
  作者：Hirose, Masayo Y.; Lahiri, Partha
  作者单位：Kyushu University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
  摘要：The two-level normal hierarchical model has played an important role in statistical theory and applications. In this article, we first introduce a general adjusted maximum likelihood method for estimating the unknown variance component of the model and the associated empirical best linear unbiased predictor of the random effects. We then discuss a new idea for selecting prior for the hyperparameters. The prior, called a multi-goal prior, produces Bayesian solutions for hyperparmeters and rando...
• Optimal Estimation of Wasserstein Distance on a Tree With an Application to Microbiome Studies AI翻译
  作者：Wang, Shulei; Cai, T. Tony; Li, Hongzhe
  作者单位：University of Pennsylvania; University of Pennsylvania
  摘要：The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the sampled data in th...
• Rejoinder-A Gibbs Sampler for a Class of Random Convex Polytopes AI翻译
  作者：Jacob, Pierre E.; Gong, Ruobin; Edlefsen, Paul T.; Dempster, Arthur P.
  作者单位：Harvard University; Rutgers University System; Rutgers University New Brunswick; Fred Hutchinson Cancer Center; ESSEC Business School
  摘要：We are very grateful to all commenters for their stimulating remarks, questions, as well as useful pointers to the literature which span a wide range of statistical methods over decades of research. We have neither the space nor the knowledge to answer many of the questions raised, and we only aim to offer some clarifications. We hope that readers will be as enthusiastic as ourselves about research on the topics discussed by the commenters. In the following, we refer to Diaconis and Wang as DW...
• What are the Most Important Statistical Ideas of the Past 50 Years? AI翻译
  作者：Gelman, Andrew; Vehtari, Aki
  作者单位：Columbia University; Aalto University
  摘要：We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss key contributions in these subfields, how they relate to modern computing and big data, and how they might be developed and exten...
• An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls AI翻译
  作者：Chernozhukov, Victor; Wuthrich, Kaspar; Zhu, Yinchu
  作者单位：Massachusetts Institute of Technology (MIT); University of California System; University of California San Diego; Brandeis University
  摘要：We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against missp...
• The Effects of Stringent and Mild Interventions for Coronavirus Pandemic AI翻译
  作者：Tian, Ting; Tan, Jianbin; Luo, Wenxiang; Jiang, Yukang; Chen, Minqiong; Yang, Songpan; Wen, Canhong; Pan, Wenliang; Wang, Xueqin
  作者单位：Sun Yat Sen University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatme...
• More Efficient Policy Learning via Optimal Retargeting AI翻译
  作者：Kallus, Nathan
  作者单位：Cornell University; Cornell University
  摘要：Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned policies. We study a solution to this problem based on retargeting, that is, changing the population on which policies are optimized. We first argue that at the population level,...