Journal of the American Statistical Association

最新文章

• Network Dependence Can Lead to Spurious Associations and Invalid Inference AI翻译
  作者：Lee, Youjin; Ogburn, Elizabeth L.
  作者单位：University of Pennsylvania; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative...
• On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls AI翻译
  作者：Ferman, Bruno
  摘要：We consider the asymptotic properties of the synthetic control (SC) estimator when both the number of pretreatment periods and control units are large. If potential outcomes follow a linear factor model, we provide conditions under which the SC unit asymptotically recovers the factor structure of the treated unit, even when the pretreatment fit is imperfect. This happens when there are weights diluted among an increasing number of control units such that a weighted average of the factor struct...
• Comment on A Gibbs Sampler for a Class of Random Convex Polytopes, by Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen, and Arthur P. Dempster AI翻译
  作者：Shafer, Glenn
  作者单位：Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick
• Stochastic Gradient Markov Chain Monte Carlo AI翻译
  作者：Nemeth, Christopher; Fearnhead, Paul
  作者单位：Lancaster University
  摘要：Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that performing exact inference generally requires all of the data to be processed at each iteration of the algorithm. For large datasets, the computational cost of MCMC can be prohibitive, which has led to recent developments in scalable Monte Carlo algorithms that h...
• Rare Feature Selection in High Dimensions AI翻译
  作者：Yan, Xiaohan; Bien, Jacob
  摘要：It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such rare features has received little attention despite its prevalence in diverse areas, ranging from natural language processing (e.g., rare words) to biology (e.g., rare species). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can...
• Spherical Regression Under Mismatch Corruption With Application to Automated Knowledge Translation AI翻译
  作者：Shi, Xu; Li, Xiaoou; Cai, Tianxi
  作者单位：University of Michigan System; University of Michigan; University of Minnesota System; University of Minnesota Twin Cities; Harvard University
  摘要：Motivated by a series of applications in data integration, language translation, bioinformatics, and computer vision, we consider spherical regression with two sets of unit-length vectors when the data are corrupted by a small fraction of mismatch in the response-predictor pairs. We propose a three-step algorithm in which we initialize the parameters by solving an orthogonal Procrustes problem to estimate a translation matrix ignoring the mismatch. We then estimate a mapping matrix aiming to c...
• A Penalized Synthetic Control Estimator for Disaggregated Data AI翻译
  作者：Abadie, Alberto; L'Hour, Jeremy
  作者单位：Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; Institut Polytechnique de Paris; ENSAE Paris
  摘要：Synthetic control methods are commonly applied in empirical research to estimate the effects of treatments or interventions on aggregate outcomes. A synthetic control estimator compares the outcome of a treated unit to the outcome of a weighted average of untreated units that best resembles the characteristics of the treated unit before the intervention. When disaggregated data are available, constructing separate synthetic controls for each treated unit may help avoid interpolation biases. Ho...
• 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