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作者：Dawid, A. Philip
作者单位：University of Cambridge
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作者：Steiner, Gregor; Steel, Mark F. J.
作者单位：University of Warwick
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作者：Wang, Ruodu
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作者：Chen, Yaqing; Lin, Shu-Chin; Zhou, Yang; Carmichael, Owen; Mueller, Hans-Georg; Wang, Jane-Ling
作者单位：Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Davis; Louisiana State University System; Louisiana State University; Pennington Biomedical Research Center
摘要：Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the...
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作者：Liang, Ziyi; Sesia, Matteo; Sun, Wenguang
作者单位：University of Southern California; University of Southern California; Zhejiang University; Zhejiang University
摘要：This paper presents a conformal inference method for out-of-distribution testing that leverages side information from labelled outliers, which are commonly underutilized or even discarded by conventional conformal p-values. This solution is practical and blends inductive and transductive inference strategies to adaptively weight conformal p-values, while also automatically leveraging the most powerful model from a collection of one-class and binary classifiers. Further, this approach leads to ...
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作者：Seaman, Shaun R.
作者单位：University of Cambridge; MRC Biostatistics Unit
摘要：Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a ...
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作者：Luo, Shikai; Yang, Ying; Shi, Chengchun; Yao, Fang; Ye, Jieping; Zhu, Hongtu
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of London; London School Economics & Political Science; Peking University; University of North Carolina; University of North Carolina Chapel Hill
摘要：The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of the average treatment effec...
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作者：Zhang, Yangfan; Yang, Yun
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
摘要：This article considers Bayesian model selection via mean-field (MF) variational approximation. Towards this goal, we study the non-asymptotic properties of MF inference that allows latent variables and model misspecification. Concretely, we show a Bernstein-von Mises (BvM) theorem for the variational distribution from MF under possible model misspecification, which implies the distributional convergence of MF variational approximation to a normal distribution centring at the maximal likelihood...
