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作者：Schervish, M. J.; Seidenfeld, Teddy; Kadane, J. B.
作者单位：Carnegie Mellon University; Carnegie Mellon University
摘要：We investigate differences between a simple Dominance Principle applied to sums of fair prices for variables and dominance applied to sums of forecasts for variables scored by proper scoring rules. In particular, we consider differences when fair prices and forecasts correspond to finitely additive expectations and dominance is applied with infinitely many prices and/or forecasts.
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作者：Cai, T. Tony; Yuan, Ming
作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison
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作者：Zhou, Shuheng
作者单位：University of Michigan System; University of Michigan
摘要：Undirected graphs can be used to describe matrix variate distributions. In this paper, we develop new methods for estimating the graphical structures and underlying parameters, namely, the row and column covariance and inverse covariance matrices from the matrix variate data. Under sparsity conditions, we show that one is able to recover the graphs and covariance matrices with a single random matrix from the matrix variate normal distribution. Our method extends, with suitable adaptation, to t...
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作者：Jiang, Ci-Ren; Yu, Wei; Wang, Jane-Ling
作者单位：Academia Sinica - Taiwan; Roche Holding; Roche Holding USA; Genentech; University of California System; University of California Davis
摘要：Sliced inverse regression (Duan and Li [Ann. Statist. 19 (1991) 505-530], Li [J. Amer. Statist. Assoc. 86 (1991) 316-342]) is an appealing dimension reduction method for regression models with multivariate covariates. It has been extended by Ferro and Yao [Statistics 37 (2003) 475-488, Statist. Sinica 15 (2005) 665-683] and Hsing and Ren [Ann. Statist. 37 (2009) 726-755] to functional covariates where the whole trajectories of random functional covariates are completely observed. The focus of ...
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作者：Wang, Li; Xue, Lan; Qu, Annie; Liang, Hua
作者单位：University System of Georgia; University of Georgia; Oregon State University; University of Illinois System; University of Illinois Urbana-Champaign; George Washington University
摘要：We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of the proposed method is its capability of handling model selection in cases where it is difficult to specify the likelihood function. We derive the qu...
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作者：Buehlmann, Peter; Meier, Lukas; van de Geer, Sara
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
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作者：Buja, A.; Brown, L.
作者单位：University of Pennsylvania
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作者：Belloni, Alexandre; Chernozhukov, Victor; Wang, Lie
作者单位：Duke University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
摘要：We propose a self-tuning root Lasso method that simultaneously resolves three important practical problems in high-dimensional regression analysis, namely it handles the unknown scale, heteroscedasticity and (drastic) non-Gaussianity of the noise. In addition, our analysis allows for badly behaved designs, for example, perfectly collinear regressors, and generates sharp bounds even in extreme cases, such as the infinite variance case and the noiseless case, in contrast to Lasso. We establish v...
