Journal of the American Statistical Association

最新文章

• Inference on Selected Subgroups in Clinical Trials AI翻译
  作者：Guo, Xinzhou; He, Xuming
  作者单位：University of Michigan System; University of Michigan
  摘要：When existing clinical trial data suggest a promising subgroup, we must address the question of how good the selected subgroup really is. The usual statistical inference applied to the selected subgroup, assuming that the subgroup is chosen independent of the data, may lead to an overly optimistic evaluation of the selected subgroup. In this article, we address the issue of selection bias and develop a de-biasing bootstrap inference procedure for the best selected subgroup effect. The proposed...
• Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression AI翻译
  作者：Sun, Qiang; Zhang, Heping
  作者单位：University of Toronto; Yale University
  摘要：Analysis of high-dimensional data has received considerable and increasing attention in statistics. In practice, we may not be interested in every variable that is observed. Instead, often some of the variables are of particular interest, and the remaining variables are nuisance. To this end, we propose the nuisance penalized regression which does not penalize the parameters of interest. When the coherence between interest parameters and nuisance parameters is negligible, we show that resultin...
• 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...
• Privacy-Preserving Parametric Inference: A Case for Robust Statistics AI翻译
  作者：Avella-Medina, Marco
  作者单位：Columbia University
  摘要：Differential privacy is a cryptographically motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm, one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting, we introduce a general framework for par...
• Statistical Inference for Online Decision Making: In a Contextual Bandit Setting AI翻译
  作者：Chen, Haoyu; Lu, Wenbin; Song, Rui
  作者单位：North Carolina State University
  摘要：Online decision making problem requires us to make a sequence of decisions based on incremental information. Common solutions often need to learn a reward model of different actions given the contextual information and then maximize the long-term reward. It is meaningful to know if the posited model is reasonable and how the model performs in the asymptotic sense. We study this problem under the setup of the contextual bandit framework with a linear reward model. The epsilon-greedy policy is a...
• Evaluating Proxy Influence in Assimilated Paleoclimate Reconstructions-Testing the Exchangeability of Two Ensembles of Spatial Processes AI翻译
  作者：Harris, Trevor; Li, Bo; Steiger, Nathan J.; Smerdon, Jason E.; Narisetty, Naveen; Tucker, J. Derek
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Columbia University; United States Department of Energy (DOE); Sandia National Laboratories
  摘要：Climate field reconstructions (CFRs) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To...
• Metropolized Knockoff Sampling AI翻译
  作者：Bates, Stephen; Candes, Emmanuel; Janson, Lucas; Wang, Wenshuo
  作者单位：Stanford University; Stanford University; Harvard University
  摘要：Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This article introduces techniques for knockoff generation in gr...
• Fast Calibrated Additive Quantile Regression AI翻译
  作者：Fasiolo, Matteo; Wood, Simon N.; Zaffran, Margaux; Nedellec, Raphael; Goude, Yannig
  作者单位：University of Bristol; Institut Polytechnique de Paris; ENSTA Paris; Electricite de France (EDF)
  摘要：We propose a novel framework for fitting additive quantile regression models, which provides well-calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those usable with distributional generalized additive models, while maintaining equivalent numerical efficiency and stability. The proposed methods are at once statistically rigorous and computationally efficient, because they are based on the general b...