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作者：Liu, Bang; Yang, Run; Zhou, Fan
作者单位：Universite de Montreal; Shanghai University of Finance & Economics
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作者：Wang, Xuewei; Tang, Rui (Sammi)
摘要：Large language models (LLMs) are making waves for efficient and reproducible data analysis. We congratulate the authors on developing an impressive LLM-based data analysis system (Sun et al. 2025) that makes statistical and machine learning tools more accessible for users across diverse backgrounds. LAMBDA offers a remarkable contribution to this space by well-designing a dual-agent and code-free architecture for interactive data analysis. In contrast to fully autonomous LLM agents, the open-s...
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作者：Liu, Bang; Yang, Run; Zhou, Fan
作者单位：Universite de Montreal; Shanghai University of Finance & Economics
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作者：Wang, Xuewei; Tang, Rui (Sammi)
摘要：Large language models (LLMs) are making waves for efficient and reproducible data analysis. We congratulate the authors on developing an impressive LLM-based data analysis system (Sun et al. 2025) that makes statistical and machine learning tools more accessible for users across diverse backgrounds. LAMBDA offers a remarkable contribution to this space by well-designing a dual-agent and code-free architecture for interactive data analysis. In contrast to fully autonomous LLM agents, the open-s...
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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...
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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...
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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...
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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...
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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...
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作者：Lee, Seunghyun; Gu, Yuqi
作者单位：Columbia University
摘要：In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparameterized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose interpretable deep generative models for rich data types with discrete lat...
