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作者:Tallman, Emily; West, Mike
作者单位:Duke University; Duke University
摘要:Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations, and defines new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison, and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian ...
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作者:Liang, Faming; Kim, Sehwan; Sun, Yan
作者单位:Purdue University System; Purdue University; Harvard University; Harvard Medical School; University of Pennsylvania
摘要:While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set-'inferring the uncertainty of model parameters on the basis of observations'-has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. Extended Fiduci...
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作者:Rizzelli, Stefano
作者单位:University of Padua
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作者:Mateu, Jorge
作者单位:Universitat Jaume I
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作者:Neu, Gergely
作者单位:Pompeu Fabra University
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 bett...
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作者:Antoniano-Villalobos, Isadora
作者单位:Universita Ca Foscari Venezia
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作者:Huk, David; Pacchiardi, Lorenzo; Dutta, Ritabrata; Steel, Mark
作者单位:University of Warwick; University of Oxford
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作者:Cao, Yang; Sun, Xinwei; Yao, Yuan
作者单位:Hong Kong University of Science & Technology; Fudan University
摘要:Controlling the False Discovery Rate (FDR) in a variable selection procedure is critical for reproducible discoveries, and it has been extensively studied in sparse linear models. However, it remains largely open in scenarios where the sparsity constraint is not directly imposed on the parameters but on a linear transformation of the parameters to be estimated. Examples of such scenarios include total variations, wavelet transforms, fused LASSO, and trend filtering. In this paper, we propose a...
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作者:Tian, Ye; Xu, Hongquan
作者单位:Beijing University of Posts & Telecommunications; University of California System; University of California Los Angeles
摘要:Space-filling designs are widely used in computer experiments. A minimum aberration-type space-filling criterion was recently proposed to rank and assess a family of space-filling designs including orthogonal array-based Latin hypercubes and strong orthogonal arrays. However, it is difficult to apply the criterion in practice because it requires intensive computation for determining the space-filling pattern, which measures the stratification properties of designs on various subregions. In thi...
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作者:Hoff, Peter; McCormack, Andrew; Zhang, Anru R.
作者单位:Duke University; Duke University
摘要:A separable covariance model can describe the among-row and among-column correlations of a random matrix and permits likelihood-based inference with a very small sample size. However, if the assumption of separability is not met, data analysis with a separable model may misrepresent important dependence patterns in the data. As a compromise between separable and unstructured covariance estimation, we decompose a covariance matrix into a separable component and a complementary 'core' covariance...