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作者：Cressie, Noel
作者单位：University of Wollongong
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作者：Lee, Chung Eun; Shao, Xiaofeng
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article, we introduce a new methodology to perform dimension reduction for a stationary multivariate time series. Our method is motivated by the consideration of optimal prediction and focuses on the reduction of the effective dimension in conditional mean of time series given the past information. In particular, we seek a contemporaneous linear transformation such that the transformed time series has two parts with one part being conditionally mean independent of the past. To achieve ...
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作者：Reid, Stephen; Taylor, Jonathan; Tibshirani, Robert
作者单位：Stanford University
摘要：Applied statistical problems often come with prespecified groupings to predictors. It is natural to test for the presence of simultaneous group-wide signal for groups in isolation, or for multiple groups together. Current tests for the presence of such signals include the classical F-test or a t-test on unsupervised group prototypes (either group centroids or first principal components). In this article, we propose test statistics that aim for power improvements over these classical approaches...
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作者：Roth, Aaron
作者单位：University of Pennsylvania
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作者：Murray, Thomas A.; Yuan, Ying; Thall, Peter F.
作者单位：University of Texas System; UTMD Anderson Cancer Center
摘要：Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This article describes a new approach for optimizing dynamic treatment regimes, which bridges the gap between Bayesian inference and existing approaches, like Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, i...
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作者：Ranganath, Rajesh; Blei, David M.
作者单位：Princeton University; Columbia University; Columbia University
摘要：We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from completely random measures, a Poisson-process-based construction in which the atom weights are independent. Completely random measures imply strong independence assumptions in the corresponding hierarchical model, and these assumpt...
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作者：Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.
作者单位：University of Miami; University of Michigan System; University of Michigan; University of Chicago
摘要：Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be quite sensitive to tuning parameter choices. We study the effects of bias correction on confidence interval coverage in the context of kernel density and local polynomial regression estimation, and prove that bias correction can be preferred to undersmoothing for minimizing coverage error and increasing robustness to t...
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作者：Glynn, Adam N.; Kashin, Konstantin
作者单位：Emory University; Harvard University
摘要：We demonstrate that the front-door adjustment can be a useful alternative to standard covariate adjustments (i.e., back-door adjustments), even when the assumptions required for the front-door approach do not hold. We do this by providing asymptotic bias formulas for the front-door approach that can be compared directly to bias formulas for the back-door approach. In some cases, this allows the tightening of bounds on treatment effects. We also show that under one-sided noncompliance, the fron...
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作者：Han, Fang; Liu, Han
作者单位：University of Washington; University of Washington Seattle; Princeton University
摘要：We present a robust alternative to principal component analysis (PCA)called elliptical component analysis (ECA)for analyzing high-dimensional, elliptically distributed data. ECA estimates the eigenspace of the covariance matrix of the elliptical data. To cope with heavy-tailed elliptical distributions, a multivariate rank statistic is exploited. At the model-level, we consider two settings: either that the leading eigenvectors of the covariance matrix are nonsparse or that they are sparse. Met...
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作者：Li, Bing; Solea, Eftychia
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We introduce a nonparametric graphical model whose observations on vertices are functions. Many modern applications, such as electroencephalogram and functional magnetic resonance imaging (fMRI), produce data are of this type. The model is based on additive conditional independence (ACI), a statistical relation that captures the spirit of conditional independence without resorting to multi-dimensional kernels. The random functions are assumed to reside in a Hilbert space. No distributional ass...
