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作者:Wang, Changhu; Ge, Xinzhou; Song, Dongyuan; Li, Jingyi Jessica
作者单位:University of California System; University of California Los Angeles; Oregon State University; University of California System; University of California Los Angeles
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作者:Bian, Zeyu; Shi, Chengchun; Qi, Zhengling; Wang, Lan
作者单位:University of Miami; University of London; London School Economics & Political Science; George Washington University
摘要:This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions-temporal stationarity and individual homogeneity are both violated. To handle the double inhomogeneities, we propose a class of latent factor models for the reward and transition functions, under which we develop a general OPE framework that consists of both model-based and model-free approaches. To our knowledge, this is the first article that develops statistically sound ...
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作者:Wang, Y. Samuel; Kolar, Mladen; Drton, Mathias
作者单位:Cornell University; University of Southern California; Mohamed bin Zayed University of Artificial Intelligence MBZUAI; Technical University of Munich
摘要:Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. A primary challenge in causal discovery of directed acyclic graphs is determining a causal ordering among the variables, and our work offers a framework for cons...
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作者:McCartan, Cory; Fisher, Robin; Goldin, Jacob; Ho, Daniel E.; Imai, Kosuke
作者单位:Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of the Treasury; University of Chicago; National Bureau of Economic Research; Stanford University; Stanford University; Harvard University; Harvard University
摘要:Estimating racial disparities without access to individual-level racial information is a common challenge in economic and policy settings. We develop a statistical method that relaxes the strong independence assumption of common race imputation approaches like Bayesian-Improved Surname Geocoding (BISG). Our identification assumption is that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. The proposed approach ...
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作者:Potiron, Yoann; Volkov, Vladimir
作者单位:Keio University; University of Tasmania; HSE University (National Research University Higher School of Economics)
摘要:A novel statistical approach to estimating latency, defined as the time it takes to learn about an event and generate response to this event, is proposed. Our approach only requires a multidimensional point process describing event times, which circumvents the use of more detailed datasets which may not even be available. We consider the class of parametric Hawkes models capturing clustering effects and define latency as a known function of kernel parameters, typically the mode of kernel funct...
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作者:Kirichenko, Alisa; Kelly, Luke J.; Koskela, Jere
作者单位:University of Warwick; University College Cork; Newcastle University - UK
摘要:We derive tractable criteria for the consistency of Bayesian tree reconstruction procedures, which constitute a central class of algorithms for inferring common ancestry among DNA sequence samples in phylogenetics. Our results encompass several Bayesian algorithms in widespread use, such as BEAST, MrBayes, and RevBayes. Unlike essentially all existing asymptotic guarantees for tree reconstruction, we require no discretization or boundedness assumptions on branch lengths. Our results are also v...
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作者:Zhao, Bangyao; Huggins, Jane E.; Kang, Jian
作者单位:University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要:Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary use...
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作者:Bertolacci, Michael; Zammit-Mangion, Andrew; Giraldo, Juan Valderrama; O'Neill, Michael; Bransby, Fraser; Watson, Phil
作者单位:University of Western Australia; University of Wollongong; University of Western Australia
摘要:For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of the seabed sediments at a proposed site. However, data collection offshore is costly, so analysis of the seabed sediments must be made from measurements that are spatially sparse. Adding to this challenge, the structure of the seabed sediments exhibits both nonstationarity and anisotropy. To address these issues, we propose GeoWarp, a hierarchical spatial statis...
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作者:Koh, Jonathan; Koch, Erwan; Davison, Anthony C.
作者单位:University of Bern; University of Lausanne; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要:Severe thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are favorable to severe weather, and both they and the composite variable PROD=CAPExSRH can be used as indicators of severe thunderstorm activity. Their extremal spatial dependence exhibits temporal non-stationarity due to seasonality and large-scale atmospheric signals such as El Ni & ntilde;o-Southern...
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作者:Wang, Bingyan; Fan, Jianqing
作者单位:Princeton University
摘要:This article studies noisy low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of theoretical understanding when the observations are contaminated by heavy-tailed noises. Prior theory falls short of explaining the empirical results and is unable to ca...