2014

最新文章

• GAUSSIAN APPROXIMATION OF SUPREMA OF EMPIRICAL PROCESSES AI翻译
  作者：Chernozhukov, Victor; Chetverikov, Denis; Kato, Kengo
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of California System; University of California Los Angeles; University of Tokyo
  摘要：This paper develops a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating whole empirical processes in the sup-norm. We prove an abstract approximation theorem applicable to a wide variety of statistical problems, such as construction of uniform confidence bands for functions. Notably, the bound in the main approximation theorem is nonasymptotic and the theorem allows for functions...
• We provide a correction to the expression for scoring Gaussian directed acyclic graphical models derived in Geiger and Heckerman (vol 30, pg 1414, 2002) AI翻译
  作者：Kuipers, Jack; Moffa, Giusi; Heckerman, David
  作者单位：University of Regensburg; University of Regensburg; Microsoft
• ON ASYMPTOTICALLY OPTIMAL CONFIDENCE REGIONS AND TESTS FOR HIGH-DIMENSIONAL MODELS AI翻译
  作者：Van de Geer, Sara; Buehlmann, Peter; Ritov, Ya'acov; Dezeure, Ruben
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; Hebrew University of Jerusalem
  摘要：We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in a high-dimensional model. It can be easily adjusted for multiplicity taking dependence among tests into account. For linear models, our method is essentially the same as in Zhang and Zhang [J. R. Stat. Soc. Ser. B Stat. Methodol. 76 (2014) 217-242]: we analyze its asymptotic properties and establish its asymptotic optimality in terms of...
• DISCUSSION: A SIGNIFICANCE TEST FOR THE LASSO AI翻译
  作者：Lv, Jinchi; Zheng, Zemin
  作者单位：University of Southern California; University of Southern California
• CO-CLUSTERING SEPARATELY EXCHANGEABLE NETWORK DATA AI翻译
  作者：Choi, David; Wolfe, Patrick J.
  作者单位：Carnegie Mellon University; University of London; University College London
  摘要：This article establishes the performance of stochastic blockmodels in addressing the co-clustering problem of partitioning a binary array into subsets, assuming only that the data are generated by a nonparametric process satisfying the condition of separate exchangeability. We provide oracle inequalities with rate of convergence O-P(n(-1/4)) corresponding to profile likelihood maximization and mean-square error minimization, and show that the blockmodel can be interpreted in this setting as an...
• VARIABLE SELECTION FOR GENERAL INDEX MODELS VIA SLICED INVERSE REGRESSION AI翻译
  作者：Jiang, Bo; Liu, Jun S.
  作者单位：Harvard University
  摘要：Variable selection, also known as feature selection in machine learning, plays an important role in modeling high dimensional data and is key to data-driven scientific discoveries. We consider here the problem of detecting influential variables under the general index model, in which the response is dependent of predictors through an unknown function of one or more linear combinations of them. Instead of building a predictive model of the response given combinations of predictors, we model the...
• AN ADAPTIVE COMPOSITE QUANTILE APPROACH TO DIMENSION REDUCTION AI翻译
  作者：Kong, Efang; Xia, Yingcun
  作者单位：University of Kent; National University of Singapore
  摘要：Sufficient dimension reduction [J. Amer Statist. Assoc. 86 (1991) 316-342] has long been a prominent issue in multivariate nonparametric regression analysis. To uncover the central dimension reduction space, we propose in this paper an adaptive composite quantile approach. Compared to existing methods, (1) it requires minimal assumptions and is capable of revealing all dimension reduction directions; (2) it is robust against outliers and (3) it is structure-adaptive, thus more efficient. Asymp...
• NONPARAMETRIC MAXIMUM LIKELIHOOD APPROACH TO MULTIPLE CHANGE-POINT PROBLEMS AI翻译
  作者：Zou, Changliang; Yin, Guosheng; Feng, Long; Wang, Zhaojun
  作者单位：Nankai University; University of Hong Kong; Nankai University
  摘要：In multiple change-point problems, different data segments often follow different distributions, for which the changes may occur in the mean, scale or the entire distribution from one segment to another. Without the need to know the number of change-points in advance, we propose a nonparametric maximum likelihood approach to detecting multiple change-points. Our method does not impose any parametric assumption on the underlying distributions of the data sequence, which is thus suitable for det...
• BAYESIAN VARIABLE SELECTION WITH SHRINKING AND DIFFUSING PRIORS AI翻译
  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...
• ADAPTIVE PIECEWISE POLYNOMIAL ESTIMATION VIA TREND FILTERING AI翻译
  作者：Tibshirani, Ryan J.
  作者单位：Carnegie Mellon University
  摘要：We study trend filtering, a recently proposed tool of Kim et al. [SIAM Rev. 51 (2009) 339-360] for nonparametric regression. The trend filtering estimate is defined as the minimizer of a penalized least squares criterion, in which the penalty term sums the absolute kth order discrete derivatives over the input points. Perhaps not surprisingly, trend filtering estimates appear to have the structure of kth degree spline functions, with adaptively chosen knot points (we say appear here as trend f...