2021

最新文章

• Discussion of A Gibbs Sampler for a Class of Random Convex Polytopes AI翻译
  作者：Diaconis, Persi; Wang, Guanyang
  作者单位：Stanford University; Rutgers University System; Rutgers University New Brunswick
• Discussion of Exponential-Family Embedding With Application to Cell Developmental Trajectories for Single-Cell RNA-seq Data AI翻译
  作者：Ji, Zhicheng; Ji, Hongkai
  作者单位：Duke University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
• Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning AI翻译
  作者：Li, Sijia; Li, Xiudi; Luedtke, Alex
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• Biased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical Outcomes AI翻译
  作者：Fogarty, Colin B.; Lee, Kwonsang; Kelz, Rachel R.; Keele, Luke J.
  作者单位：Massachusetts Institute of Technology (MIT); Sungkyunkwan University (SKKU); University of Pennsylvania; University of Pennsylvania
  摘要：We investigate the efficacy of surgical versus nonsurgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instrumental variable design with surgeons' tendencies to operate as an instrument. Assuming instrument validity, we find that nonsurgical alternatives can reduce both hospital length of stay and the risk of complications, with estimated effects larger for septic patients than for nonseptic patients. The validity of our in...
• Evaluation of the Health Impacts of the 1990 Clean Air Act Amendments Using Causal Inference and Machine Learning AI翻译
  作者：Nethery, Rachel C.; Mealli, Fabrizia; Sacks, Jason D.; Dominici, Francesca
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Florence; United States Environmental Protection Agency
  摘要：We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the total events avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number obs...
• A New Coefficient of Correlation AI翻译
  作者：Chatterjee, Sourav
  作者单位：Stanford University
  摘要：Is it possible to define a coefficient of correlation which is (a) as simple as the classical coefficients like Pearson's correlation or Spearman's correlation, and yet (b) consistently estimates some simple and interpretable measure of the degree of dependence between the variables, which is 0 if and only if the variables are independent and 1 if and only if one is a measurable function of the other, and (c) has a simple asymptotic theory under the hypothesis of independence, like the classic...
• Trends in Extreme Value Indices AI翻译
  作者：de Haan, Laurens; Zhou, Chen
  作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; European Central Bank; De Nederlandsche Bank NV; Tinbergen Institute
  摘要：We consider extreme value analysis for independent but nonidentically distributed observations. In particular, the observations do not share the same extreme value index. Assuming continuously changing extreme value indices, we provide a nonparametric estimate for the functional extreme value index. Besides estimating the extreme value index locally, we also provide a global estimator for the trend and its joint asymptotic theory. The asymptotic theory for the global estimator can be used for ...
• Discussion on Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data AI翻译
  作者：Datta, Jyotishka; Mukherjee, Bhramar
  作者单位：Virginia Polytechnic Institute & State University; University of Michigan System; University of Michigan
• Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman-Pearson Approach to Text Classification AI翻译
  作者：Xia, Lucy; Zhao, Richard; Wu, Yanhui; Tong, Xin
  作者单位：Hong Kong University of Science & Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; University of Hong Kong; University of Southern California; University of Southern California
  摘要：This article addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large Type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious pr...
• Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes AI翻译
  作者：Wang, Zhuo; Jiang, Yujing; Wan, Hui; Yan, Jun; Zhang, Xuebin
  作者单位：Shenzhen University; Colorado State University System; Colorado State University Fort Collins; Environment & Climate Change Canada; University of Connecticut
  摘要：Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here, we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location ...