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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：Mateu, Jorge
作者单位：Universitat Jaume I
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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...
