4期

最新文章

• Confidence bands in nonparametric time series regression AI翻译
  作者：Zhao, Zhibiao; Wul, Wei Biao
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Chicago
  摘要：We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models. Simultaneous confidence bands are constructed and the coverage probabilities are shown to be asymptotically correct. The imposed dependence structure allows applications in many linear and nonlinear autoregressive processes. The results are applied to the S&P 500 Index data.
• Discussion: One-step sparse estimates in nonconcave penalized likelihood models AI翻译
  作者：Zhang, Cun-Hui
  作者单位：Rutgers University System; Rutgers University New Brunswick
• Dimension reduction based on constrained canonical correlation and variable filtering AI翻译
  作者：Zhou, Jianhui; He, Xuming
  作者单位：University of Virginia; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The curse of dimensionality has remained a challenge for high-dimensional data analysis in statistics'. The sliced inverse regression (SIR) and canonical correlation (CANCOR) methods aim to reduce the dimensionality of data by replacing the explanatory variables with a small number of composite directions without losing much information. However, the estimated composite directions generally involve all of the variables, making their interpretation difficult. To simplify the direction estimates...
• Discussion:: One-step sparse estimates in nonconcave penalized likelihood models AI翻译
  作者：Buehlmann, Peter; Meier, Lukas
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
• Semiparametric detection of significant activation for brain fMRI AI翻译
  作者：Zhang, Chunming; Yu, Tao
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Functional magnetic resonance imaging (fMRI) aims to locate activated regions in human brains when specific tasks are performed. The conventional tool for analyzing fMRI data applies some variant of the linear model, which is restrictive in modeling assumptions. To yield more accurate prediction of the time-course behavior of neuronal responses, the semiparametric inference for the underlying hemodynamic response function is developed to identify significantly activated voxels. Under mild regu...
• A wavelet whittle estimator of the memory parameter of a nonstationary Gaussian time series AI翻译
  作者：Moulines, E.; Roueff, F.; Taqqu, M. S.
  作者单位：IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris; Boston University
  摘要：We consider a time series X = (X-k, k is an element of Z) with memory parameter d(0) is an element of R. This time series is either stationary or can be made stationary after differencing a finite number of times. We study the local Whittle wavelet estimator of the memory parameter d(0). This is a wavelet-based semiparametric pseudo-likelihood maximum method estimator. The estimator may depend on a given finite range of scales or on a range which becomes infinite with the sample size. We show ...
• Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models AI翻译
  作者：Zou, Hui; Li, Runze
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• A multivariate central limit theorem for randomized orthogonal array sampling designs in computer experiments AI翻译
  作者：Loh, Wei-Liem
  作者单位：National University of Singapore
  摘要：Let f : [0, 1)(d) -> R be an integrable function. An objective of many computer experiments is to estimate integral(d)([0, 1)) f (x) dx by evaluating f at a finite number of points in [0, 1)(d). There is a design issue in the choice of these points and a popular choice is via the use of randomized orthogonal arrays. This article proves a multivariate central limit theorem for a class of randomized orthogonal array sampling designs [Owen Statist. Sinica 2 (1992a) 439-452] as well as for a class...
• The sparsity and bias of the lasso selection in high-dimensional linear regression AI翻译
  作者：Zhang, Cun-Hui; Huang, Jian
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Iowa
  摘要：Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436-1462] showed that, for neighborhood selection in Gaussian graphical models, under a neighborhood stability condition, the LASSO is consistent, even when the number of variables is of greater order than the sample size. Zhao and Yu [(2006) J. Machine Learning Research 7 2541-2567] formalized the neighborhood stability condition in the context of linear regression as a strong irrepresentable condition. That paper showed that under this condi...
• Asymptotic equivalence for nonparametric regression with multivariate and random design AI翻译
  作者：Reiss, Markus
  作者单位：Ruprecht Karls University Heidelberg
  摘要：We show that nonparametric regression is asymptotically equivalent, in Le Cam's sense, to a sequence of Gaussian white noise experiments as the number of observations tends to infinity. We propose a general constructive framework, based on approximation spaces, which allows asymptotic equivalence to be achieved, even in the cases of multivariate and random design.