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作者：Su, Buxin; Zhang, Jiayao; Collina, Natalie; Yan, Yuling; Li, Didong; Cho, Kyunghyun; Fan, Jianqing; Roth, Aaron; Su, Weijie
作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; New York University; Princeton University
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作者：Chen, Qihui
作者单位：The Chinese University of Hong Kong, Shenzhen
摘要：This article presents a general framework for estimating high-dimensional conditional latent factor models via constrained nuclear norm regularization. We establish large sample properties of the estimators and provide efficient algorithms for their computation. To improve practical applicability, we propose a cross-validation procedure for selecting the regularization parameter. Our framework unifies the estimation of various conditional factor models, enabling the derivation of new asymptoti...
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作者：Tang, Yifu; Kirch, Claudia; Lee, Jeong Eun; Meyer, Renate
作者单位：University of Otago; Otto von Guericke University; University of Auckland
摘要：Stationarity plays a pivotal role in time series analysis. It is not only the basis for the derivation of general asymptotic theory but it also allows an efficient analysis in the frequency domain via the Whittle likelihood, based on the asymptotic independence of the Fourier coefficients. However, many regularly sampled data derived from the observation of physical or ecological processes, for instance, are only locally stationary. They exhibit slowly evolving spectra and asymptotically non-v...
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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：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：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...
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作者：Dharmakeerthi, Kulunu; Hur, YoonHaeng; Liang, Tengyuan
作者单位：University of Chicago; University of Chicago
摘要：Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often driven by unobserved confounding, and can in fact alter the concept of which model is best. This article studies distribution shifts in the domain adaptation problem with unobserved confounding. We postulate a linear structural causal model to account for endogeneity and unobserved confounding, and we le...
