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作者：Lv, Jinchi; Zheng, Zemin
作者单位：University of Southern California; University of Southern California
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作者：Narisetty, Naveen Naidu; He, Xuming
作者单位：University of Michigan System; University of Michigan
摘要：We consider a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space. We adopt the well-known spike and slab Gaussian priors with a distinct feature, that is, the prior variances depend on the sample size through which appropriate shrinkage can be achieved. We show the strong selection consistency of the proposed method in the sense that the...
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作者：Soltanolkotabi, Mahdi; Elhamifar, Ehsan; Candes, Emmanuel J.
作者单位：Stanford University; University of California System; University of California Berkeley; Stanford University
摘要：Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired by sparse subspace clustering (SSC) [In IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009) 2790-2797] to cluster noisy data, and develops some novel theory demonstrating its correctness. In particular, the theory uses ideas from geometric functional analysis to show that the ...
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作者：Wasserman, Larry
作者单位：Carnegie Mellon University
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作者：Fan, Jianqing; Ke, Zheng Tracy
作者单位：Princeton University
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作者：Sun, Yan; Yan, Hongjia; Zhang, Wenyang; Lu, Zudi
作者单位：Shanghai University of Finance & Economics; University of York - UK; University of Southampton
摘要：Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamic model, which extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the house price. A profile likelihood based estimation procedure is proposed. The asymptotic normality of the proposed estimators are derived. We also investigate how to identify the parametric/nonparametric components in the proposed semiparametric model. We show how m...
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作者：Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert
作者单位：Simon Fraser University; Stanford University; Carnegie Mellon University; Stanford University
摘要：In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in ...
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作者：Jankowski, Hanna
作者单位：York University - Canada
摘要：Under the assumption that the true density is decreasing, it is well known that the Grenander estimator converges at rate n(1/3) if the true density is curved [Sankhya Ser. A 31 (1969) 23-36] and at rate n(1/2) if the density is flat [Ann. Probab. 11 (1983) 328-345; Canad. J. Statist. 27 (1999) 557-566]. In the case that the true density is misspecified, the results of Patilea [Ann. Statist. 29 (2001) 94-123] tell us that the global convergence rate is of order n1/3 in Hellinger distance. Here...
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作者：Wang, Jiangyan; Liu, Rung; Cheng, Fuxia; Yang, Lijian
作者单位：Soochow University - China; University System of Ohio; University of Toledo; Illinois State University
摘要：We propose kernel estimator for the distribution function of unobserved errors in autoregressive time series, based on residuals computed by estimating the autoregressive coefficients with the Yule Walker method. Under mild assumptions, we establish oracle efficiency of the proposed estimator, that is, it is asymptotically as efficient as the kernel estimator of the distribution function based on the unobserved error sequence itself. Applying the result of Wang, Cheng and Yang [J. Nonparametr....
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作者：Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert
作者单位：Simon Fraser University; Stanford University; Carnegie Mellon University; Stanford University
