536期

最新文章

• 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...
• 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...
• Sampling Algorithms for Discrete Markov Random Fields and Related Graphical Models AI翻译
  作者：Izenman, Alan Julian
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：Discrete Markov random fields are undirected graphical models in which the nodes of a graph are discrete random variables with values usually represented by colors. Typically, graphs are taken to be square lattices, although more general graphs are also of interest. Such discrete MRFs have been studied in many disciplines. We describe the two most popular types of discrete MRFs, namely the two-state Ising model and the q-state Potts model, and variations such as the cellular automaton, the cel...
• A Semiparametric Kernel Independence Test With Application to Mutational Signatures AI翻译
  作者：Lee, DongHyuk; Zhu, Bin
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：Cancers arise owing to somatic mutations, and the characteristic combinations of somatic mutations form mutational signatures. Despite many mutational signatures being identified, mutational processes underlying a number of mutational signatures remain unknown, which hinders the identification of interventions that may reduce somatic mutation burdens and prevent the development of cancer. We demonstrate that the unknown cause of a mutational signature can be inferred by the associated signatur...
• Bayesian Projected Calibration of Computer Models AI翻译
  作者：Xie, Fangzheng; Xu, Yanxun
  作者单位：Johns Hopkins University
  摘要：We develop a Bayesian approach called the Bayesian projected calibration to address the problem of calibrating an imperfect computer model using observational data from an unknown complex physical system. The calibration parameter and the physical system are parameterized in an identifiable fashion via the L-2-projection. The physical system is imposed a Gaussian process prior distribution, which naturally induces a prior distribution on the calibration parameter through the L-2-projection con...
• Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation AI翻译
  作者：Kellogg, Maxwell; Mogstad, Magne; Pouliot, Guillaume A.; Torgovitsky, Alexander
  作者单位：National Bureau of Economic Research; University of Chicago; National Bureau of Economic Research; University of Chicago
  摘要：The synthetic control (SC) method is widely used in comparative case studies to adjust for differences in pretreatment characteristics. SC limits extrapolation bias at the potential expense of interpolation bias, whereas traditional matching estimators have the opposite properties. This complementarity motives us to propose a matching and synthetic control (or MASC) estimator as a model averaging estimator that combines the standard SC and matching estimators. We show how to use a rolling-orig...
• Rejoinder: Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data AI翻译
  作者：Quick, Corbin; Dey, Rounak; Lin, Xihong
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
• IFAA: Robust Association Identification and Inference for Absolute Abundance in Microbiome Analyses AI翻译
  作者：Li, Zhigang; Tian, Lu; O'Malley, A. James; Karagas, Margaret R.; Hoen, Anne G.; Christensen, Brock C.; Madan, Juliette C.; Wu, Quran; Gharaibeh, Raad Z.; Jobin, Christian; Li, Hongzhe
  作者单位：State University System of Florida; University of Florida; Stanford University; Dartmouth College; Dartmouth College; State University System of Florida; University of Florida; University of Pennsylvania
  摘要：The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AAs) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA...
• Forecasting Unemployment Using Internet Search Data via PRISM AI翻译
  作者：Yi, Dingdong; Ning, Shaoyang; Chang, Chia-Jung; Kou, S. C.
  作者单位：Williams College; National University of Singapore; Harvard University
  摘要：Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users' Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method Penalized Regression with Inferred Seasonality Module (PRISM), which uses publicly available online search data from Google. PRISM is a semiparametric method, motivated by a general state-...