2026

最新文章

• Localizing Strictly Proper Scoring Rules AI翻译
  作者：de Punder, Ramon F. A.; Diks, Cees G. H.; Laeven, Roger J. A.; van Dijk, Dick J. C.
  作者单位：University of Amsterdam; Tinbergen Institute; Tilburg University; Eindhoven University of Technology; Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
  摘要：When comparing predictive distributions, forecasters are typically not equally interested in all regions of the outcome space. To address the demand for focused forecast evaluation, we propose a procedure to transform strictly proper scoring rules into their localized counterparts while preserving the score divergence and strict propriety. This is accomplished by applying the original scoring rule to a censored distribution. Our procedure nests the censored likelihood score as a special case. ...
• Testing and Support Recovery in Population-Based Image Data AI翻译
  作者：Qu, Lianqiang; Huang, Jian; Sun, Liuquan; Zhu, Hongtu
  作者单位：Central China Normal University; Central China Normal University; Hong Kong Polytechnic University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：In this article, we propose a multiscale adaptive test to detect differences between two samples of intrinsically smoothed image data in high-dimensional context. The test aggregates data from nearby locations using adaptive weights, significantly enhancing statistical power. We demonstrate that the test statistic converges to a Gumbel extreme value distribution under the null hypothesis. Moreover, we investigate its multiscale nature, showing that the chosen scales can grow at a specific poly...
• 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...
• Patterns in Spatio-Temporal Extremes AI翻译
  作者：Oesting, Marco; Huser, Raphael
  作者单位：University of Stuttgart; University of Stuttgart; King Abdullah University of Science & Technology
  摘要：In environmental science applications, extreme events frequently exhibit a complex spatio-temporal structure, which is difficult to describe flexibly and estimate in a computationally efficient way using state-of-art parametric extreme-value models. In this article, we propose a computationally-cheap nonparametric approach to investigate the probability distribution of temporal clusters of spatial extremes, and study within-cluster patterns with respect to various characteristics. These includ...
• Dynamic Decision Making With Individualized Variable Selection AI翻译
  作者：Cai, Bryan; Cui, Ying; Fu, Haoda; Lloyd-Jones, Donald M.; Zhao, Lihui; Lu, Tian
  作者单位：Stanford University; Stanford University; Eli Lilly; Northwestern University; Feinberg School of Medicine
  摘要：Physicians today have access to a variety of tests for diagnosing and prognosticating medical conditions. Ideally, they would apply a high-quality prediction model using all relevant features to facilitate appropriate decision-making (e.g., treatment selection; risk assessment). However, some of these features incur additional costs and are not readily available to patients and physicians. In practice, predictors are typically gathered sequentially, that is, physicians continually evaluate inf...
• A Factor-Copula Latent-Vine Time Series Model for Extreme Flood Insurance Losses AI翻译
  作者：Li, Xiaoting; Joe, Harry; Genest, Christian
  作者单位：University of British Columbia; McGill University
  摘要：Statistical inference on the dependence of multivariate extremes poses notable challenges, particularly in contexts characterized by large dimensions and sparse extreme observations. While copula models provide flexible parametric methods for dependence modeling, caution is warranted when using them for extremal dependence inference or tail extrapolation. In this article, a novel class of factor-vine copula models is introduced. It is designed for modeling the dependence of extreme insurance l...
• Improved Bounds and Inference on Optimal Regimes AI翻译
  作者：Laurendeau, Julien D.; Sarvet, Aaron L.; Stensrud, Mats J.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Massachusetts System; University of Massachusetts Amherst
  摘要：Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, they can guide practical decisions based on formal decision theoretic criteria. Here we derive new results on optimal treatment regimes in settings where the effect of interest is bounded. These results are driven by consideration of superopti...
• Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine AI翻译
  作者：Chen, Canyi; Qiao, Nan; Zhu, Liping
  作者单位：University of Michigan System; University of Michigan; Renmin University of China; Renmin University of China
  摘要：This article concerns efficiently classifying high-dimensional data over decentralized networks. Penalized support vector machines (SVMs) are widely used for high-dimensional classification tasks. However, the double nonsmoothness of the objective function poses significant challenges in developing efficient decentralized learning methods. Existing approaches frequently suffer from slow, sublinear convergence rates. To address this issue, we consider a convolution-based smoothing technique for...
• Double Generalized Linear Models: Likelihood and Bayesian Methods AI翻译
  作者：Ni, Yang
  作者单位：University of Texas System; University of Texas Austin