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作者：Cook, RD; Setodji, CM
作者单位：University of Minnesota System; University of Minnesota Twin Cities; RAND Corporation
摘要：We propose a test of dimension in multivariate regression. This test is in the spirit of tests on the rank of the coefficient matrix in a multivariate linear model, but it does not require a prespecified model. The test may be particularly useful at the outset of an analysis before a multivariate model is posited, because it can lead to low-dimensional summary plots that are inferred to contain all of the sample information on the multivariate mean function.
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作者：Parmigiani, G; Ashih, HW; Samsa, GR; Duncan, PW; Lai, SM; Matchar, DB
作者单位：Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Duke University; Duke University; Duke University; University of Kansas; University of Kansas; Duke University
摘要：It is common to assess disability of stroke patients using standardized scales, such as the Rankin Stroke Outcome Scale (RS) and the Barthel Index (BI). The RS, which was designed for applications to stroke, is based on assessing directly the global conditions of a patient. The BI, which was designed for more general applications, is based on a series of questions about the patient's ability to carry out 10 basic activities of daily living. Because both scales are commonly used, but few studie...
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作者：Muthén, B; Jo, B; Brown, CH
作者单位：University of California System; University of California Los Angeles; State University System of Florida; University of South Florida
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作者：Holmes, CC; Mallick, BK
作者单位：Imperial College London; Texas A&M University System; Texas A&M University College Station
摘要：A Bayesian method is presented for the nonparametric modeling of univariate and multivariate non-Gaussian response data. Data-adaptive multivariate regression splines are used where the number and location of the knot points are treated as random. The posterior model space is explored using a reversible-jump Markov chain Monte Carlo sampler. Computational difficulties are partly alleviated by introducing a random residual effect in the model that leaves many of the posterior conditional distri...
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作者：Andersen, TG; Bollerslev, T; Diebold, FX; Labys, P
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作者：Dodd, LE; Pepe, MS
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); University of Washington; University of Washington Seattle
摘要：Medical advances continue to provide new and potentially better means for detecting disease. Such is true in cancer, for example, where biomarkers are sought for early detection and where improvements in imaging methods may pick up the initial functional and molecular changes associated with cancer development. In other binary classification tasks, computational algorithms such as neural networks, support vector machines, and evolutionary algorithms have been applied to areas as diverse as cre...
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作者：Barnard, J; Frangakis, CE; Hill, JL; Rubin, DB
作者单位：Columbia University; Harvard University; Johns Hopkins University
摘要：The precarious state of the educational system in the inner cities of the United States, as well as its potential causes and solutions, have been popular topics of debate in recent years. Part of the difficulty in resolving this debate is the lack of solid empirical evidence regarding the true impact of educational initiatives. The efficacy of so-called school choice programs has been a particularly. contentious issue. A current multimillion dollar program,,the School Choice Scholarship Founda...
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作者：Gelfand, AE; Kim, HJ; Sirmans, CF; Banerjee, S
作者单位：Duke University; University of Oulu; University of Connecticut; University of Minnesota System; University of Minnesota Twin Cities
摘要：In many applications, the objective is to build regression models to explain a response variable over a region of interest under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coefficients are assumed to be constant over the region. However, in some applications, coefficients are expected to vary at the local or subregional level. Here we focus on the local case. Although parametric modeling of the spatial surface for the coefficient is p...
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作者：Krueger, AB; Zhu, P
作者单位：Princeton University
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作者：Bühlmann, P; Yu, B
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of California System; University of California Berkeley
摘要：This article investigates a computationally simple variant of boosting, L(2)Boost, which is constructed from a functional gradient descent algorithm with the L-2-loss function. Like other boosting algorithms, L(2)Boost uses many times in an iterative fashion a prechosen fitting method, called the learner. Based on the explicit expression of refitting of residuals of L(2)Boost, the case with (symmetric) linear learners is studied in detail in both regression and classification. In particular, w...
