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作者：Tang, Yin; Li, Bing
作者单位：University of Kentucky; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the conditional distribution and the conditional mean as the targets of estimation. This unified framework is achieved by a specially structured neural network-the Belted and Ensembled Neural Network (BENN)-that consists of a narrow latent layer, which we call the belt, and a family of transformations of the response, w...
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作者：Spagnolo, Francesco Schirripa; Salvati, Nicola; Bertarelli, Gaia; Haziza, David; Chambers, Ray
作者单位：University of Pisa; Universita Ca Foscari Venezia; University of Ottawa; Australian National University
摘要：Projective outlier-robust M-quantile-based small area estimators can be substantially biased when the sample data contain representative outliers. In this article we propose two new predictive type bias corrected versions of these estimators for continuous and discrete outcomes. Given both area level and individual level outliers in the population, these new estimators are more efficient than the robust-predictive and robust-projective estimators that have been proposed in the small area estim...
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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：Legrand, Juliette; Naveau, Philippe; Oesting, Marco
作者单位：Centre National de la Recherche Scientifique (CNRS); Universite de Bretagne Occidentale; CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); University of Stuttgart; University of Stuttgart
摘要：Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this article, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the to...
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作者：Zhang, Heng; Liu, Zhi-Ping
作者单位：Shandong University; Shandong University
摘要：The acquisition of multimodal functional data, wherein functional data from multiple modalities are concurrently recorded for a single subject, has emerged as a promising strategy for constructing more accurate gene regulatory networks (GRNs). While ordinary differential equation (ODE) methods have been extensively used for modeling GRNs, the existing methods are primarily tailored for single-modal data. In this article, we introduce a multilinear ODE (ML-ODE) framework that integrates ODE-bas...
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作者：Shen, Tao; Wang, Wanjie
作者单位：National University of Singapore; National University of Singapore
摘要：Modern data often arise with multiple modalities. For example, covariates and a network are observed on the same subjects, and both contain useful information. Effectively integrating these modalities is important and challenging, especially when the response is unavailable. We study the fundamental covariate selection problem for high-dimensional data by leveraging network information. We propose the Network-Guided Covariate Selection (NGCS) algorithm. NGCS exploits the spectral structure of ...
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作者：Liu, Qiao; Wong, Wing Hung
作者单位：Yale University; Yale University; Stanford University
摘要：Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and out...
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作者：Tan, Jianbin; Shi, Pixu; Zhang, Anru R.
作者单位：Duke University; Duke University
摘要：Trajectory data, including time series and longitudinal measurements, are increasingly common in health-related domains such as biomedical research and epidemiology. Real-world trajectory data frequently exhibit heterogeneity across subjects such as patients, sites, and subpopulations, yet many traditional methods are not designed to accommodate such heterogeneity in data analysis. To address this, we propose a unified framework, termed Functional Singular Value Decomposition (FSVD), for stati...
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作者：Bai, Yuehao; Huang, Shunzhuang; Moon, Sarah; Shaikh, Azeem M.; Vytlacil, Edward J.
作者单位：University of Southern California; University of Chicago; Massachusetts Institute of Technology (MIT); University of Chicago; Yale University
摘要：In the context of a binary outcome, treatment, and instrument, Balke and Pearl establish that the monotonicity condition of Imbens and Angrist has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This article shows that this phenomenon...
