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作者：Fan, Yiwei; Lu, Xiaoling; Liu, Yufeng; Zhao, Junlong
作者单位：Renmin University of China; University of North Carolina; University of North Carolina Chapel Hill; Beijing Normal University
摘要：Hierarchical classification problems are commonly seen in practice. However, most existing methods do not fully use the hierarchical information among class labels. In this article, a novel label embedding approach is proposed, which keeps the hierarchy of labels exactly, and reduces the complexity of the hypothesis space significantly. Based on the newly proposed label embedding approach, a new angle-based classifier is developed for hierarchical classification. Moreover, to handle massive da...
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作者：Yang, Jun; Zhou, Zhou
作者单位：University of Toronto
摘要：We develop a unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and...
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作者：Banerjee, Sudipto
作者单位：University of California System; University of California Los Angeles
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作者：Fiksel, Jacob; Datta, Abhirup; Amouzou, Agbessi; Zeger, Scott
作者单位：Johns Hopkins University; Johns Hopkins University
摘要：Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test po...
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作者：Mai, Qing; Zhang, Xin; Pan, Yuqing; Deng, Kai
作者单位：State University System of Florida; Florida State University
摘要：Modern scientific studies often collect datasets in the form of tensors. These datasets call for innovative statistical analysis methods. In particular, there is a pressing need for tensor clustering methods to understand the heterogeneity in the data. We propose a tensor normal mixture model approach to enable probabilistic interpretation and computational tractability. Our statistical model leverages the tensor covariance structure to reduce the number of parameters for parsimonious modeling...
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作者：Zhang, Likun; Shaby, Benjamin A.; Wadsworth, Jennifer L.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Colorado State University System; Colorado State University Fort Collins; Lancaster University
摘要：Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally prohibitive, even in moderate dimensions, due to the necessity of repeatedly evaluating the multivariate Gaussian distribution function. In this work, we attempt to achieve truly high-dimensional inference for extremes of spatial processes, while retaining the desirable flexibility in the tail dependence structure, by mo...
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作者：Linton, Oliver B.; Tang, Haihan
作者单位：University of Cambridge; Fudan University
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作者：Frigau, Luca; Wu, Qiuyi; Banks, David
作者单位：University of Cagliari; University of Rochester; Duke University
摘要：Sometimes the Joint Statistical Meetings (JSM) is frustrating to attend, because multiple sessions on the same topic are scheduled at the same time. This article uses seeded latent Dirichlet allocation and a scheduling optimization algorithm to very significantly reduce overlapping content in the original schedule for the 2020 JSM program. Specifically, a measure based on total variation distance that ranges from 0 (random scheduling) to 1 (no overlapping content) finds that the original sched...
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作者：Ciarleglio, Adam; Petkova, Eva; Harel, Ofer
作者单位：George Washington University; New York University; New York University; University of Connecticut
摘要：Frontal power asymmetry (FA), a measure of brain function derived from electroencephalography, is a potential biomarker for major depressive disorder (MDD). Though FA is functional in nature, it is typically reduced to a scalar value prior to analysis, possibly obscuring its relationship with MDD and leading to a number of studies that have provided contradictory results. To overcome this issue, we sought to fit a functional regression model to characterize the association between FA and MDD s...
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作者：Chen, Xi; Lee, Jason D.; Li, He; Yang, Yun
作者单位：New York University; Princeton University; University of Illinois System; University of Illinois Urbana-Champaign
摘要：The growing size of modern datasets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This article studies distributed estimation for a fundamental statistical machine learning problem, principal component analysis (PCA). Despite the massive literature on top eigenvector estimation, much less is presented for the top-L-dim (L > 1) eigenspace estimation, especially in a distributed manner. We propose a novel multi-round algo...
