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作者：Zhang, Cun-Hui
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized variable selection in high-dimensional linear regression. The LASSO is fast and continuous, but biased. The bias of the LASSO may prevent consistent variable selection. Subset selection is unbiased but computationally costly. The MC+ has two elements: a minimax concave penalty (MCP) and a penalized linear unbiased selection (PLUS) algorithm. The MCP provides the convexity of the penalized loss in sparse region...
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作者：Anderes, Ethan
作者单位：University of California System; University of California Davis
摘要：We present fixed domain asymptotic results that establish consistent estimates of the variance and scale parameters for a Gaussian random field with a geometric anisotropic Matern autocovariance in dimension d > 4. When d < 4 this is impossible due to the mutual absolute continuity of Matern Gaussian random fields with different scale and variance (see Zhang [J. Amer. Statist. Assoc. 99 (2004) 250-261]). Informally, when d > 4, we show that one can estimate the coefficient on the principle irr...
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作者：Jensen, Jens Ledet
作者单位：Aarhus University
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作者：Hall, Peter; Pham, Tung
作者单位：University of Melbourne
摘要：We show that scale-adjusted versions of the centroid-based classifier enjoys optimal properties when used to discriminate between two very high-dimensional populations where the principal differences are in location. The scale adjustment removes the tendency of scale differences to confound differences in means. Certain other distance-based methods, for example, those founded on nearest-neighbor distance, do not have optimal performance in the sense that we propose. Our results permit varying ...
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作者：Kyung, Minjung; Gill, Jeff; Casella, George
作者单位：State University System of Florida; University of Florida; Washington University (WUSTL)
摘要：We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision parameter of the Dirichlet process...
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作者：Kong, Linglong; Mizera, Ivan
作者单位：University of Alberta
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作者：Li, Bing; Kim, Min Kyung; Altman, Naomi
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We consider dimension reduction for regression or classification in which the predictors are matrix- or array-valued. This type of predictor arises when measurements are obtained for each combination of two or more underlying variables-for example, the voltage measured at different channels and times in electroencephalography data. For these applications, it is desirable to preserve the array structure of the reduced predictor (e.g., time versus channel), but this cannot be achieved within the...
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作者：Jensen, Jens Ledet; Fuh, Cheng-Der
作者单位：National Central University; Academia Sinica - Taiwan
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作者：Hallin, Marc; Paindaveine, Davy; Siman, Miroslav
作者单位：Universite Libre de Bruxelles
摘要：A new multivariate concept of quantile, based on a directional version of Koenker and Bassett's traditional regression quantiles, is introduced for multivariate location and multiple-output regression problems. In their empirical version, those quantiles can be Computed efficiently via linear programming techniques. Consistency, Bahadur representation and asymptotic normality results are established. Most importantly, the contours generated by those quantiles are shown to coincide with the cla...
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作者：Kalogeropoulos, Konstantinos; Roberts, Gareth O.; Dellaportas, Petros
作者单位：University of London; London School Economics & Political Science; University of Warwick; Athens University of Economics & Business
摘要：We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrization defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The method...
