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作者：Lindon, Michael; Ham, Dae Woong; Tingley, Martin; Bojinov, Iavor
作者单位：Netflix, Inc.; Microsoft; Harvard University
摘要：Linear models are foundational tools in statistics and ubiquitous across the applied sciences. However, conventional statistical tests, such as t-tests and F-tests, are only valid at fixed sample sizes, making them unsuitable for sequential settings such as online A/B testing. We develop an anytime-valid theory of inference for the linear model, introducing sequential analogues of classical tests and confidence sets that provide Type-I error control and coverage guarantees uniformly over all s...
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作者：Zhang, Xinyu; Zhou, Wei; Liu, Jingyuan; Kang, Jian
作者单位：Xiamen University; Southwestern University of Finance & Economics - China; Xiamen University; University of Michigan System; University of Michigan
摘要：High-dimensional mediation analysis has gained increasing interest in various fields, particularly in genetic and medical research. Compared with existing works that focus mainly on high-dimensional mediators, this article advocates a new framework of Partial Regularization-based Inference for Mediation Effects (PRIME) when both exposures and mediators are high-dimensional. Estimated direct and indirect effects are established using a group-wise partially penalized least squares method, incorp...
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作者：Del Sole, Claudio; Lijoi, Antonio; Prunster, Igor
作者单位：University of Milano-Bicocca; Bocconi University
摘要：Competing risks occur in survival analysis when multiple causes of death are present. They play a prominent role in several domains extending beyond biostatistics to encompass epidemiology, actuarial sciences, and reliability theory. This article adopts a multi-state modeling framework to competing risks. We introduce a class of flexible nonparametric priors, defined through hierarchical completely random measures, to model the transition probabilities, and identify the specific (conditionally...
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作者：Zhang, Linjun; Li, Lexin
作者单位：Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Berkeley
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作者：Tian, Yuang; Sun, Jiajin; He, Yinqiu
作者单位：Hong Kong University of Science & Technology; State University System of Florida; Florida State University; University of Wisconsin System; University of Wisconsin Madison
摘要：This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent...
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作者：Yu, Ruoqi; Ding, Peng
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
摘要：Many scientific questions in biomedical, environmental, and psychological research involve understanding the effects of multiple factors on outcomes. While factorial experiments are ideal for this purpose, randomized controlled treatment assignment is generally infeasible in many empirical studies. Therefore, investigators must rely on observational data, where drawing reliable causal inferences for multiple factors remains challenging. As the number of treatment combinations grows exponential...
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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...
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作者：Chib, Siddhartha; Shimizu, Kenichi
作者单位：Washington University (WUSTL); University of Alberta
摘要：A common assumption in the fitting of unordered multinomial response models for J mutually exclusive categories is that the responses arise from the same set of J categories across subjects. However, when responses measure a choice made by the subject, it is more appropriate to condition the distribution of multinomial responses on a subject-specific consideration set, drawn from the power set of {1,2,& mldr;,J}. This leads to a mixture of multinomial response models governed by a probability ...
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作者：Li, Yiluan; Wang, Qiyu; Feng, Zekang; Wang, Xueqin; Tang, Zheng-Zheng
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Wisconsin System; University of Wisconsin Madison
摘要：High-dimensional compositional data are ubiquitous in omics research. Microbiome sequencing experiments measure the relative abundances (proportions) of microbial features, while the absolute abundances within the ecosystem remain unobserved. Most regression methods with microbial relative abundance predictors rely on log-ratio transformations and typically impose sparsity on the regression coefficients to manage high dimensionality. However, we show that the sparsity assumption often does not...
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作者：Babii, Andrii; Carrasco, Marine; Tsafack, Idriss
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Universite de Montreal
摘要：We study the linear regression model with a scalar response and a functional predictor, a canonical example of an ill-posed inverse problem. We show that the functional partial least-squares (PLS) estimator achieves convergence rates that are nearly minimax-optimal over a class of ellipsoids and propose an adaptive early-stopping procedure for selecting the number of PLS components. In addition, we develop a new test that detects parametric local alternatives. The test can be inverted to const...
