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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：Wang, Zhanfeng; Pan, Rui; Wang, Xueqin; Wang, Yuedong
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; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of California System; University of California Santa Barbara
摘要：Many methods have been developed to analyze complex data, such as non-Euclidean shape, network, and manifold data. However, there is a lack of methods for studying interactions among complex data. In this article, we first propose a novel kernel function for a metric space and construct its associated reproducing kernel Hilbert space. The new nonstationary kernel function provides a flexible and powerful tool for learning complex structures in non-Euclidean data. We then construct an analysis ...
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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：Waggoner, Philip
作者单位：Columbia University
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作者：Wang, Lijun; Zhao, Hongyu
作者单位：Yale University
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作者：Zheng, Runbing; Tang, Minh
作者单位：Johns Hopkins University; North Carolina State University
摘要：Motivated by the increasing demand for multi-source data integration in various scientific fields, in this article we study matrix completion in scenarios where the data exhibits certain block-wise missing structures-specifically, where only a few noisy submatrices representing (overlapping) parts of the full matrix are available. We propose the Chain-linked Multiple Matrix Integration (CMMI) procedure to efficiently combine the information that can be extracted from these individual noisy sub...
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作者：Wang, Yibo; Lee, Sunghee; Elliott, Michael R.
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its nonrandom sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the accuracy of the reporte...
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作者：Cai, Leheng; Guo, Xu; Lian, Heng; Zhu, Liping
作者单位：Tsinghua University; Beijing Normal University; City University of Hong Kong; Renmin University of China
摘要：High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this critical issue, high-dimensional convoluted rank regression has been recently proposed, introducing penalized convoluted rank regression estimators. However, these developed estimators cannot be directly used to make inference. In this article, we i...
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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...
