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作者：Qiu, Jiaming; Zhao, Ying-Qi; Wei, John; Chinnaiyan, Arul M.; Tosoian, Jeffrey; Zheng, Yingye
作者单位：Fred Hutchinson Cancer Center; University of Michigan System; University of Michigan; Vanderbilt University
摘要：Binary medical decision-making increasingly demands sequential diagnostic strategies that optimize accuracy while minimizing patient burden and healthcare costs. In prostate cancer diagnosis, many patients undergo unnecessary biopsies despite existing biomarkers and imaging tests that already inform risk stratification. Sequential testing, where tests are selectively administered based on prior results, offers a promising approach to balance diagnostic power with procedural efficiency. We prop...
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作者：Zeng, Jing; Min, Keqian; Mai, Qing
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; State University System of Florida; Florida State University
摘要：Sliced inverse regression (SIR) is a flexible modeling tool that effectively reduces dimensions to reveal the complicated mechanism behind data. In recent years, SIR has been generalized to high dimensions in a variety of ways. However, all existing methods rely on the light-tailed assumption for predictors, which is frequently violated in real life. To tackle ubiquitous heavy-tailed data, we propose a novel robust SIR method, referred to as ROSE, that scales well with high dimensions and heav...
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作者：Meng, Xuran; Li, Yi
作者单位：University of Michigan System; University of Michigan
摘要：While deep neural networks (DNNs) are used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalized nonparametric regression models (GNRMs) and developing a rigorous inference framework. Unlike existing approaches that assume independence between estimation errors and inputs to establish the error bound, a condition often violated in GNRMs, we allow for ...
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作者：Nolde, Natalia; Zhou, Chen; Zhou, Menglin
作者单位：University of British Columbia; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
摘要：In this article we address the problem of high quantile estimation conditional on a related variable being extreme. The problem set-up is of interest in a number applications to evaluate tail risk of a focal variable in the tail of a conditioning variable. A primary example we consider is the assessment of systemic risk in financial markets using a risk measure known as the conditional value-at-risk (CoVaR). The proposed estimator is based on a novel approach to handle the bivariate tail depen...
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作者：Jeong, Yujin; Rothenhausler, Dominik
作者单位：Stanford University
摘要：Many existing approaches for estimating parameters in settings with distributional shifts operate under an invariance assumption. For example, under covariate shift, it is assumed that p(y|x) remains invariant. We refer to such distribution shifts as sparse, since they may be substantial but affect only a part of the data generating system. In contrast, in various real-world settings, shifts might be dense. More specifically, these dense distributional shifts may arise through numerous small a...
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作者：Wang, Kehan; Chen, Yuexin; Han, Yixin; Xu, Wangli; Kong, Linglong
作者单位：Renmin University of China; Renmin University of China; University of Alberta
摘要：Controlling the false discovery rate (FDR) in high-dimensional multiple testing has recently been advanced through mirror statistics via knockoff and data splitting. However, these approaches primarily emphasize the symmetry structure of the one-dimensional mirror statistics while inadvertently overlooking the distribution information from non-null features when determining the rejection region, potentially causing a power loss. To tackle this challenge, we present a novel framework termed sym...
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作者：Chen, Elynn; Fan, Jianqing; Zhu, Xiaonan
作者单位：New York University; Princeton University
摘要：We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, using techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm offers an accelerated...
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作者：Martin, Ryan
作者单位：North Carolina State University
摘要：An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher's fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn't require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfo...
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作者：Yang, Yuepeng; Ma, Cong
作者单位：Yale University; University of Chicago
摘要：The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals' latent traits and their binary responses to assessments or questionnaires. In this article, we introduce a new likelihood-based estimator-random pairing maximum likelihood estimator (RP-MLE ) and its bootstrapped variant multiple random pairing MLE (MRP-MLE ) which faithfully estimate the item parameters in the Rasch model. The new estimators have sever...
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作者：Wang, Y. Samuel; Kolar, Mladen; Drton, Mathias
作者单位：Cornell University; University of Southern California; Mohamed bin Zayed University of Artificial Intelligence MBZUAI; Technical University of Munich
摘要：Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. A primary challenge in causal discovery of directed acyclic graphs is determining a causal ordering among the variables, and our work offers a framework for cons...
