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作者:Power, Sam
作者单位:University of Bristol
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作者:Fong, Edwin; Holmes, Chris; Walker, Stephen G.
作者单位:Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin; University of Oxford
摘要:The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we present a different perspective that focuses on missing observations as the source of statistical uncertainty, with the parameter of interest being known precisely given the entire population. We argue that the foundation of Bayesian inference is to assign a distribution on missing observations conditional on what has been observed. In the i.i.d. setting with an observed sample of size n, the Bayesia...
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作者:Porcu, Emilio; White, Philip A.; Genton, Marc G.
作者单位:Khalifa University of Science & Technology; Berry Consultants, LLC; Brigham Young University; King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
摘要:The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalised network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different temporal instants. We provide new classes of stationary nonseparable space-time covariance functions where space can be a generalised netw...
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作者:Nemeth, Christopher
作者单位:Lancaster University
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作者:Wilkinson, Darren
作者单位:Durham University
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作者:Stark, Philip B.
作者单位:University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要:We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form empirical-Bernstein CSs and CIs (converging exactly to the oracle Bernstein width) as well as non-closed-form betting CSs and CIs. Our method combines new composite nonnegative (super) martingales with Ville's maximal inequality, with strong connections to testing by bet...
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作者:Chai, Christine P.
作者单位:Microsoft
摘要:We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes. Tests based on e-values are safe, i.e. they preserve type-I error guarantees, under such optional continuation. We define growth rate optimality (GAO) as an analogue of power in an optional continuation context, and we show ...
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作者:Ly, Alexander
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作者:Crane, Harry; Xu, Min
作者单位:Rutgers University System; Rutgers University New Brunswick
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作者:Yang, Qing; Tong, Xin
作者单位:Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California