Journal of the American Statistical Association

最新文章

• Balanced Sampling With Inequalities: Application to Category Bounding, Matrix Rounding, and Spread Sampling AI翻译
  作者：Tripet, Arnaud; Tille, Yves
  作者单位：University of Neuchatel
  摘要：In this article, we propose a novel algorithm for balanced sample selection with linear inequality constraints, ensuring that estimators remain within fixed bounds. This algorithm extends the cube method of Deville and Till & eacute;, allowing the selection of a sample from a database where Horvitz-Thompson estimators of totals are equal or nearly equal to the true population totals. The new algorithm has several key applications, including imposing minimum sample sizes for small areas and con...
• The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data AI翻译
  作者：Chen, Chixiang; Chen, Shuo; Ye, Zhenyao; Shi, Xu; Ma, Tianzhou; Shardell, Michelle
  作者单位：University System of Maryland; University of Maryland Baltimore; University of Michigan System; University of Michigan; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland Baltimore
  摘要：Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic dat...
• Sparse Bayesian Multidimensional Item Response Theory AI翻译
  作者：Li, Jiguang; Gibbons, Robert; Rockova, Veronika
  作者单位：University of Chicago; University of Chicago
  摘要：Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our article develops a Bayesian platform for binary and ordinal item MIRT which requires minimal tuning and scales well on large datasets due to its parallelizable features. Bayesian methodology for MIRT models has traditionally ...
• Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies AI翻译
  作者：Wu, Dongxiao; Li, Xinran
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago
  摘要：Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducti...
• When Composite Likelihood meets Stochastic Approximation AI翻译
  作者：Alfonzetti, Giuseppe; Bellio, Ruggero; Chen, Yunxiao; Moustaki, Irini
  作者单位：University of Udine; University of London; London School Economics & Political Science
  摘要：A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inferences when the likelihood function of a statistical model is computationally intractable. While composite likelihood has computational advantages, it can still be demanding when dealing with numerous likelihood components and a large sample size. This article tackles this challenge by employing an approximation of the conventional c...
• Comment on Data Fission: Splitting a Single Data Point AI翻译
  作者：Waggoner, Philip
  作者单位：Columbia University
• Comments on Data Fission: Splitting a Single Data Point by James Leiner, Boyan Duan, Larry Wasserman, and Aaditya Ramdas AI翻译
  作者：Wang, Lijun; Zhao, Hongyu
  作者单位：Yale University
• Large-Scale Low-Rank Gaussian Process Prediction with Support Points AI翻译
  作者：Song, Yan; Dai, Wenlin; Genton, Marc G.
  作者单位：Renmin University of China; King Abdullah University of Science & Technology
  摘要：Low-rank approximation is a popular strategy to tackle the big n problem associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however, theoretical foundations explain...
• Efficient Multiple Change Point Detection and Localization For High-Dimensional Quantile Regression with Heteroscedasticity AI翻译
  作者：Wang, Xianru; Liu, Bin; Zhang, Xinsheng; Liu, Yufeng
  作者单位：Southwestern University of Finance & Economics - China; Fudan University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this article, we consider potential structural changes and complicated tail distributions. There are various existing methods proposed to handle either structural changes or heteroscedasticity. However, it is difficult to handle them simultaneously. To overcome this limitation, we consider statistically and computationally efficient change point detect...
• Online Policy Learning and Inference by Matrix Completion AI翻译
  作者：Duan, Congyuan; Li, Jingyang; Xia, Dong
  作者单位：Hong Kong University of Science & Technology; University of Michigan System; University of Michigan
  摘要：Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that combines an epsilon -greedy policy for decision-making with an online gradient descent algorithm for bandit parameter estimation. Our novel ...