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作者：Janvin, Matias; Stensrud, Mats J.
作者单位：University of Oslo; Diakonhjemmet Hospital; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
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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：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：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...
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作者：Potiron, Yoann; Volkov, Vladimir
作者单位：Keio University; University of Tasmania; HSE University (National Research University Higher School of Economics)
摘要：A novel statistical approach to estimating latency, defined as the time it takes to learn about an event and generate response to this event, is proposed. Our approach only requires a multidimensional point process describing event times, which circumvents the use of more detailed datasets which may not even be available. We consider the class of parametric Hawkes models capturing clustering effects and define latency as a known function of kernel parameters, typically the mode of kernel funct...
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作者：Zou, James; Yuksekgonul, Mert
作者单位：Stanford University
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作者：Shen, Shuting; Lu, Junwei; Lin, Xihong
作者单位：National University of Singapore; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
摘要：In light of the rapidly growing large-scale data in federated ecosystems, the traditional principal component analysis (PCA) is often not applicable due to privacy protection considerations and large computational burden. Algorithms were proposed to lower the computational cost, but few can handle both high dimensionality and massive sample size under distributed settings. In this article, we propose the FAst DIstributed (FADI) PCA method for federated data when both the dimension d and the sa...
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作者：Hallin, Marc
作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles; Czech Academy of Sciences; Institute of Information Theory & Automation of the Czech Academy of Sciences
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作者：Hahn, P. Richard
作者单位：Arizona State University; Arizona State University-Tempe
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作者：Shi, Jieru; Wu, Zhenke; Dempsey, Walter
作者单位：University of Michigan System; University of Michigan
摘要：Contextual sensing and delivery of digital interventions to improve health outcomes have gained significant traction in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on the effectiveness of digital interventions where each individual is repeatedly randomized to receive treatments over numerous time points. Throughout the study, individual characteristics and contextual factors around randomization are coll...
