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作者：Delaigle, Aurore; Hall, Peter
作者单位：University of Melbourne
摘要：We consider classification of functional data when the training curves are not observed on the same interval. Different types of classifier are suggested, one of which involves a new curve extension procedure. Our approach enables us to exploit the information contained in the endpoints of these intervals by incorporating it in an explicit but flexible way. We study asymptotic properties of our classifiers, and show that, in a variety of settings, they can even produce asymptotically perfect c...
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作者：Taddy, Matt
作者单位：University of Chicago
摘要：Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article introduces a straightforward framework of sentiment-sufficient dimension reduction for text data. Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represent...
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作者：Liang, Faming; Song, Qifan; Yu, Kai
作者单位：Texas A&M University System; Texas A&M University College Station; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and co...
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作者：Page, Garritt; Bhattacharya, Abhishek; Dunson, David
作者单位：Pontificia Universidad Catolica de Chile; Indian Statistical Institute; Indian Statistical Institute Kolkata; Duke University
摘要：It has become common for datasets to contain large numbers of variables in studies conducted in areas such as genetics, machine vision, image analysis, and many others. When analyzing such data, parametric models are often too inflexible while nonparametric procedures tend to be nonrobust because of insufficient data on these high-dimensional spaces. This is particularly true when interest lies in building efficient classifiers in the presence of many predictor variables. When dealing with the...
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作者：Schennach, S. M.; Hu, Yingyao
作者单位：Brown University; Johns Hopkins University
摘要：Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g., validation data, known error distributions, repeated measurements, or instruments). In contrast, we establish that the fully nonparametric classical errors-in-variables model is identifiable from data on the regressor and the dependent variable alone, unless the model takes a very specific parametric form. This parametric family includes (but is not limite...
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作者：Shedden, Kerby; Zeng, Donglin; Wang, Yuanjia
作者单位：University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; Columbia University
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作者：Zhang, Jin; Braun, Thomas M.
作者单位：University of Michigan System; University of Michigan
摘要：In traditional schedule or dose schedule finding designs, patients are assumed to receive their assigned dose schedule combination throughout the trial even though the combination may be found to have an undesirable toxicity profile, which contradicts actual clinical practice. Since no systematic approach exists to optimize intrapatient dose schedule assignment, we propose a Phase I clinical trial design that extends existing approaches to optimize dose and schedule solely between patients by ...
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作者：Jiang, Qian; Wang, Hansheng; Xia, Yingcun; Jiang, Guohua
作者单位：National University of Singapore; Peking University; National University of Singapore
摘要：We propose a novel varying coefficient model (VCM), called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional VCM, PVCM reduces the actual number of nonparametric functions and thus has better estimation efficiency. Compared with the semivarying coefficient model (SVCM), PVCM is more flexible but with the same estimation efficiency when the number of principal functions ...
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作者：Hung, Ying; Wang, Yijie; Zarnitsyna, Veronika; Zhu, Cheng; Wu, C. F. Jeff
作者单位：Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
摘要：Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on a hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified expectation-maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framewor...
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作者：Brown, Lawrence D.; Greenshtein, Eitan; Ritov, Ya'acov
作者单位：University of Pennsylvania; Hebrew University of Jerusalem
摘要：The compound decision problem for a vector of independent Poisson random variables with possibly different means has a half-century-old solution. However, it appears that the classical solution needs smoothing adjustment. We discuss three such adjustments. We also present another approach that first transforms the problem into the normal compound decision problem. A simulation study shows the effectiveness of the procedures in improving the performance over that of the classical procedure. A r...
