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作者：Jin, Jiashun; Ke, Zheng Tracy; Tang, Jiajun; Wang, Jingming
作者单位：Carnegie Mellon University; Harvard University; University of Virginia
摘要：The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to SBM, DCBM, and MMSBM. ...
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作者：Parikh, Harsh; Ross, Rachael K.; Stuart, Elizabeth; Rudolph, Kara E.
作者单位：Johns Hopkins University; Columbia University
摘要：Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our article addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), ...
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作者：Liu, Weidong; Mao, Xiaojun; Tu, Jiyuan
作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai University of Finance & Economics
摘要：This article introduces two highly efficient distributed non-convex sparse learning algorithms. Our approach accommodates non-convexity in both the loss function and penalty, acknowledging the potential non-uniqueness of local minimizers due to the inherent non-convexity. The development of an algorithm that ensures convergence to a locally minimal solution with desired statistical properties becomes imperative in this context. To overcome this challenge, we propose a strategy involving the re...
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作者：Hamura, Yasuyuki; Irie, Kaoru; Sugasawa, Shonosuke
作者单位：Kyoto University; University of Tokyo; Keio University
摘要：Count data with zero inflation and large outliers are ubiquitous in many scientific applications. However, posterior analysis under a standard statistical model, such as Poisson or negative binomial distribution, is sensitive to such contamination. This study introduces a novel framework for Bayesian modeling of counts that is robust to both zero inflation and large outliers. In doing so, we introduce rescaled beta distribution and adopt it to absorb undesirable effects from zero and outlying ...
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作者：Pollak, Moshe
作者单位：Hebrew University of Jerusalem
摘要：In the framework of the Cusum procedure, the evolution of a false alarm has a well-understood stochastic behavior. So, if observations preceding an alarm were to exhibit a behavior that is significantly different, there would be reason to reject the hypothesis that the alarm is false. We develop a test of this difference. The method is applied to detecting a change in a Covid-19 context involving a possible increase of a mean and in a context involving a possible increase in the probability of...
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作者：Tian, Ye; Weng, Haolei; Xia, Lucy; Feng, Yang
作者单位：Columbia University; Michigan State University; Hong Kong University of Science & Technology; New York University
摘要：Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the EM algorithm that effectively uses unkn...
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作者：Huang, Zhen; Sen, Bodhisattva
作者单位：Columbia University
摘要：We propose a novel and unified framework for distribution-free testing under multivariate symmetry (that includes central symmetry, sign symmetry, spherical symmetry, etc.) based on the theory of optimal transport. Our approach leads to notions of distribution-free generalized multivariate signs, absolute ranks and signed-ranks. As a consequence, we develop analogues of the sign and Wilcoxon signed-rank tests that share many of the appealing properties of their one-dimensional counterparts. In...
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作者：Xu, Shirong; Zhang, Jingnan; Wang, Junhui
作者单位：Xiamen University; Xiamen University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese University of Hong Kong
摘要：Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this article challenges that conventional wisdom. We propose a general parametric framework for modeling ordinal paired comparisons without ties. The model adopts a generalized additive structure, featuring a link function that quantifies the preference difference between two items ...
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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：He, Shuaida; Zhang, Jiarui; Chen, Xin
作者单位：Southern University of Science & Technology; South China University of Technology
摘要：Sliced inverse regression (SIR), which includes linear discriminant analysis (LDA) as a special case, is a popular and powerful dimension reduction tool. In this article, we extend SIR to address the challenges of decentralized data, prioritizing privacy and communication efficiency. Our approach, termed as federated sliced inverse regression (FSIR), facilitates distributed computing of the sufficient dimension reduction subspace among multiple clients, solely sharing local estimates to protec...
