2008

最新文章

• Estimation for almost periodic processes (vol 34, pg 1115, 2006) AI翻译
  作者：Lii, Keh-Shin; Rosenblatt, Murray
  作者单位：University of California System; University of California Riverside; University of California System; University of California San Diego
• Statistical performance of support vector machines AI翻译
  作者：Blanchard, Gilles; Bousquet, Olivier; Massart, Pascal
  作者单位：Fraunhofer Gesellschaft; Fraunhofer Germany; Alphabet Inc.; Google Incorporated; Universite Paris Saclay
  摘要：The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes. Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical regularization procedure. From this point of view, it can also be interpreted as a model selection principle us...
• Adaptive confidence bands AI翻译
  作者：Genovese, Christopher; Wasserman, Larry
  作者单位：Carnegie Mellon University
  摘要：We show that there do not exist adaptive confidence bands for curve estimation except under very restrictive assumptions. We propose instead to construct adaptive bands that cover a surrogate function f* which is close to, but simpler than, f. The surrogate captures the significant features in f. We establish lower bounds on the width for any confidence band for f* and construct a procedure that comes within a small constant factor of attaining the lower bound for finite-samples.
• Generalizing Simes' test and Hochberg's stepup procedure AI翻译
  作者：Sarkar, Sanat K.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：In a multiple testing problem where one is willing to tolerate a few false rejections, procedure controlling the familywise error rate (FWER) can potentially be improved in terms of its ability to detect false null hypotheses by generalizing it to control the k-FWER, the probability of falsely rejecting at least k null hypotheses, for some fixed k > 1. Simes' test for testing the intersection null hypothesis is generalized to control the k-FWER weakly, that is, under the intersection null hypo...
• 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
• Rodeo: Sparse, greedy nonparametric regression AI翻译
  作者：Lafferty, John; Wasserman, Larry
  作者单位：Carnegie Mellon University; Carnegie Mellon University
  摘要：We present a greedy method for simultaneously performing local bandwidth selection and variable selection in nonparametric regression. The method starts with a local linear estimator with large bandwidths, and incrementally decreases the bandwidth of variables for which the gradient of the estimator with respect to bandwidth is large. The method-called rodeo (regularization of derivative expectation operator)-conducts a sequence of hypothesis tests to threshold derivatives, and is easy to impl...
• FINITE SAMPLE APPROXIMATION RESULTS FOR PRINCIPAL COMPONENT ANALYSIS: A MATRIX PERTURBATION APPROACH AI翻译
  作者：Nadler, Boaz
  作者单位：Weizmann Institute of Science
  摘要：Principal component analysis (PCA) is a standard tool for dimensional reduction of a set of n observations (samples), each with p variables. In this paper, using a matrix perturbation approach, we study the nonasymptotic relation between the eigenvalues and eigenvectors of PCA computed on a finite sample of size n, and those of the limiting population PCA as n -> infinity. As in machine learning, we present a finite sample theorem which holds with high probability for the closeness between the...
• Effect of mean on variance function estimation in nonparametric regression AI翻译
  作者：Wang, Lie; Brown, Lawrence D.; Cai, T. Tony; Levine, Michael
  作者单位：University of Pennsylvania; Purdue University System; Purdue University
  摘要：Variance function estimation in nonparametric regression is considered and the minimax rate of convergence is derived. We are particularly interested in the effect of the unknown mean on the estimation of the variance function. Our results indicate that, contrary to the common practice, it is not desirable to base the estimator of the variance function on the residuals from an optimal estimator of the mean when the mean function is not smooth. Instead it is more desirable to use estimators of ...
• Smooth backfitting in generalized additive models AI翻译
  作者：Yu, Kyusang; Park, Byeong U.; Mammen, Enno
  作者单位：University of Mannheim; Seoul National University (SNU)
  摘要：Generalized additive models have been popular among statisticians and data analysts in multivariate nonparametric regression with non-Gaussian responses including binary and count data. In this paper, a new likelihood approach for fitting generalized additive models is proposed. It aims to maximize a smoothed likelihood. The additive functions are estimated by solving a system of nonlinear integral equations. An iterative algorithm based on smooth backfitting is developed from the Newton-Kanto...
• MULTIPLE LOCAL WHITTLE ESTIMATION IN STATIONARY SYSTEMS AI翻译
  作者：Robinson, P. M.
  作者单位：University of London; London School Economics & Political Science
  摘要：Moving from univariate to bivariate jointly dependent long-memory time series introduces a phase parameter (gamma), at the frequency of principal interest. zeros for short-memory series gamma = 0 automatically. The latter case has also been stressed under long memory, along with the fractional differencing case gamma = (delta(2) - delta(1))pi/2, where delta(1), delta(2) are the memory parameters of the two series. We develop time domain conditions under which these are and are not relevant, an...