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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：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：Wu, Dongxiao; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago
摘要：Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducti...
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作者：Alfonzetti, Giuseppe; Bellio, Ruggero; Chen, Yunxiao; Moustaki, Irini
作者单位：University of Udine; University of London; London School Economics & Political Science
摘要：A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inferences when the likelihood function of a statistical model is computationally intractable. While composite likelihood has computational advantages, it can still be demanding when dealing with numerous likelihood components and a large sample size. This article tackles this challenge by employing an approximation of the conventional c...
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作者：Chi, Chien-Ming; Fan, Yingying; Ing, Ching-Kang; Lv, Jinchi
作者单位：Academia Sinica - Taiwan; University of Southern California; National Tsing Hua University
摘要：We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. We also generalize the robust knockoffs inference in Barber, Cand & egrave;s, and Samworth to the time series setting to relax the assumption of known covariate distribution req...
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作者：Zhang, Yangfan; Wang, Runmin; Shao, Xiaofeng
作者单位：Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article, we propose a class of L-q -norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model coefficients, and testing of component-wise independence for high-dimensional observations, among others. Under the null hypothesis, we derive asymptotic normality and independence between L-q -norm based U-statistics for several qs under mild moment and cumul...
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作者：Yuan, Yubai; Zhang, Yijiao; Shahbaba, Babak; Fortin, Norbert; Cooper, Keiland; Nie, Qing; Qu, Annie
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Fudan University; University of California System; University of California Irvine; University of California System; University of California Irvine; University of California System; University of California Irvine; University of California System; University of California Santa Barbara
摘要：Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subject...
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作者：Das, Debraj; Chatterjee, Arindam; Lahiri, S. N.
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Statistical Institute; Indian Statistical Institute Delhi; Washington University (WUSTL)
摘要：This article develops methodology for higher order accurate two-sided Bootstrap confidence intervals (CIs) in high dimensional penalized regression models using the Bootstrap. We consider a large class of penalized regression methods that satisfy the Oracle property of Fan and Li and a stronger variant of it, called the Strong Oracle property. While second order accuracy of the Bootstrap is known for both classes, it is typically not sufficient to guarantee better accuracy of two-sided Bootstr...
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作者：Bhattacharjee, Satarupa; Mueller, Hans-Georg
作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis
摘要：Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without either global linear or local linear (Riemannian) structure. In such settings the classical additive error model and distributional assumptions are unattainable. Due to the rapid advancement of technology, longitudinal data containing complex random objects, such as covariance matrices, data on Riemannian manifolds, and pro...
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作者：Zhang, Haoran; Wang, Junhui
作者单位：Southern University of Science & Technology; Chinese University of Hong Kong
摘要：Longitudinal networks consist of sequences of temporal edges among multiple nodes, where the temporal edges are observed in real-time. They have become ubiquitous with the rise of online social platforms and e-commerce, but largely under-investigated in the literature. In this article, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring sparse networks so as to...
