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作者：Gu, Yuqi; Lyu, Zhongyuan; Wang, Kaizheng
作者单位：Columbia University; Columbia University; University of Sydney
摘要：We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the commonality in the presence of unknown discrepancy, by optimizing an estimated bias-variance decomposition. It applies to a broad class of statistical models including Gaussian mix...
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作者：Zhang, Dinghuai; Bengio, Yoshua
作者单位：Mila Quebec Artificial Intelligence Institute; Universite de Montreal
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作者：Molstad, Aaron J.; Zhang, Xin
作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; Florida State University
摘要：In many modern regression applications, the response consists of multiple categorical random variables whose probability mass is a function of a common set of predictors. In this article, we propose a new method for modeling such a probability mass function in settings where the number of response variables, the number of categories per response, and the dimension of the predictor are large. Our method relies on a functional probability tensor decomposition: a decomposition of a tensor-valued ...
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作者：Ridgeway, Greg
作者单位：University of Pennsylvania
摘要：This article introduces a conditional ordinal stereotype model for estimating a police officer's latent propensity to escalate to higher-severity force options during an encounter. Propensity to escalate is the likelihood of selecting a more serious force category than peer officers confronting the same circumstances, not necessarily implying that such force is excessive or contrary to policy. The associated conditional likelihood depends solely on data from the times and places where multiple...
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作者：Chen, Yuting; Hirose, Masayo Y.; Lahiri, Partha
作者单位：Eastern Kentucky University; Kyushu University; University System of Maryland; University of Maryland College Park
摘要：We advance parametric bootstrap theory to construct highly efficient empirical best prediction intervals for small area means, achieving a coverage error rate of O(m(-3/2)) , where m is the number of areas modeled by a linear mixed normal model. For a general mixed effect model with random effects from a known but nonnormal distribution (with unknown hyperparameters), we show analytically that the empirical best linear (EBL) prediction interval maintains the same coverage error order, assuming...
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作者：Meng, Xiao-Li
作者单位：Harvard University
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作者：Kim, Beomchang; Xia, Zongqi; Das, Priyam
作者单位：Virginia Commonwealth University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Harvard University; Harvard Medical School
摘要：Treatment switching is a common occurrence in the management of Multiple Sclerosis (MS), where patients transition across various disease-modifying therapies (DMTs) due to heterogeneous treatment responses, differences in disease progression, patient characteristics, and therapy-associated adverse effects. To investigate how patient-level covariates influence the likelihood of treatment transitions among DMTs, we adopt a Markovian framework, Sparse Matrix Estimation with Covariate-Based Transi...
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作者：Lan, Shiwei; Pasha, Mirjeta; Li, Shuyi; Shen, Weining
作者单位：Arizona State University; Arizona State University-Tempe; Virginia Polytechnic Institute & State University; University of California System; University of California Irvine
摘要：Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast. Many inverse problems in data science require spatiotemporal solutions derived from a sequence of time-dependent objects with these spatial features, for example, the dynamic reconstruction of computerized tomography (CT) images with edges. Conventional methods based on Gaussian processes (GP) often fall short in providing satisf...
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作者：Liu, Ze; Zhou, Yongdao; Liu, Min-Qian
作者单位：Nankai University; Nankai University
摘要：Recently, a lot of models for order-of-addition (OofA) experiments have been proposed, as well as the corresponding designs. However, most of those researches are under specific models and/or specific criteria, and a general framework is lacked. In this article, we propose a general form of linear models for OofA experiments, called the symmetric linear models. We show that almost all popularly-used linear models for OofA experiments are equivalent to symmetric linear models, and we further pr...
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作者：Cui, Yifan; Han, Sukjin
作者单位：Zhejiang University; Zhejiang University; University of Bristol
摘要：In this article, we explore optimal treatment allocation policies that target distributional welfare. Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE). While average welfare is intuitive, it may yield undesirable allocations especially when individuals are heterogeneous (e.g., with outliers)-the very reason individualized treatments were introduced in the first place. This observation motivates us to propose an optim...
