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作者：Solea, Eftychia; Li, Bing
作者单位：University of Cyprus; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
摘要：We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive for large data. We d...
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作者：Fan, Jianqing; Liao, Yuan
作者单位：Princeton University; Rutgers University System; Rutgers University New Brunswick
摘要：Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weakly serially dependent, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases, it is difficult to analyze the eigenvectors of the data matrix. To address this issue, we propose simple es...
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作者：Mukherjee, Somabha; Agarwal, Divyansh; Zhang, Nancy R.; Bhattacharya, Bhaswar B.
作者单位：University of Pennsylvania
摘要：In this article, we propose a nonparametric graphical test based on optimal matching, for assessing the equality of multiple unknown multivariate probability distributions. Our procedure pools the data from the different classes to create a graph based on the minimum non-bipartite matching, and then utilizes the number of edges connecting data points from different classes to examine the closeness between the distributions. The proposed test is exactly distribution-free (the null distribution ...
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作者：Ni, Yang
作者单位：Texas A&M University System; Texas A&M University College Station
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作者：Sang, Hejian; Kim, Jae Kwang; Lee, Danhyang
作者单位：Alphabet Inc.; Google Incorporated; Iowa State University; University of Alabama System; University of Alabama Tuscaloosa
摘要：Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation may be subject to bias under model misspecification. In this article, we propose a novel semiparametric fractional imputation (SFI) method using Gaussian mixture models. The proposed method is computationally efficient and leads to...
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作者：Liu, Yaowu; Li, Zilin; Lin, Xihong
作者单位：Southwestern University of Finance & Economics - China; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
摘要：Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling's T-2 test, the F-test, and the empirical Bayes based score test. These classical tests, however, are not robust to the signal strength and could have a substantial loss of power when signals are weak or moderate, a situation we commonly encounter in contemporary applications. In this article, we propose a minimax op...
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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：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：Dai, Chenguang; Liu, Jun S.
作者单位：Harvard University
摘要：By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihood using the Wang-Landau algorithm. We show that a faster convergence of the proposed method can be achieved via the momentum acceleration. Two implementation strategies are detailed: (i) facilitating global jumps between the posterior and surrogate distributions via the multiple-try Metropolis (MTM); (ii) constructin...
