5期

最新文章

• Prediction in functional linear regression AI翻译
  作者：Cai, T. Tony; Hall, Peter
  作者单位：University of Pennsylvania; Australian National University
  摘要：There has been substantial recent work on methods for estimating the slope function in linear regression for functional data analysis. However, as in the case of more conventional finite-dimensional regression, much of the practical interest in the slope centers on its application for the purpose of prediction, rather than on its significance in its own right. We show that the problems of slope-function estimation, and of prediction from an estimator of the slope function, have very different ...
• Risk bounds for statistical learning AI翻译
  作者：Massart, Pascal; Nedelec, Elodie
  作者单位：Universite Paris Saclay
  摘要：We propose a general theorem providing upper bounds for the risk of an empirical risk minimizer (ERM). We essentially focus on the binary classification framework. We extend Tsybakov's analysis of the risk of an ERM under margin type conditions by using concentration inequalities for conveniently weighted empirical processes. This allows us to deal with ways of measuring the size of a class of classifiers other than entropy with bracketing as in Tsybakov's work. In particular, we derive new ri...
• Efficient prediction for linear and nonlinear autoregressive models AI翻译
  作者：Mueller, Ursula U.; Schick, Anton; Wefelmeyer, Wolfgang
  作者单位：Texas A&M University System; Texas A&M University College Station; State University of New York (SUNY) System; Binghamton University, SUNY; University of Cologne
  摘要：Conditional expectations given past observations in stationary time series are usually estimated directly by kernel estimators, or by plugging in kernel estimators for transition densities. We show that, for linear and nonlinear autoregressive models driven by independent innovations, appropriate smoothed and weighted von Mises statistics of residuals estimate conditional expectations at better parametric rates and are asymptotically efficient. The proof is based on a uniform stochastic expans...
• Poisson inverse problems AI翻译
  作者：Antoniadis, Anestis; Bigot, Jeremie
  作者单位：Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Universite de Toulouse; Universite Toulouse III - Paul Sabatier
  摘要：In this paper we focus on nonparametric estimators in inverse problems for Poisson processes involving the use of wavelet decompositions. Adopting an adaptive wavelet Galerkin discretization, we find that our method combines the well-known theoretical advantages of wavelet-vaguelette decompositions for inverse problems in terms of optimally adapting to the unknown smoothness of the solution, together with the remarkably simple closed-form expressions of Galerkin inversion methods. Adapting the...
• Conditional growth charts - Discussion AI翻译
  作者：Wei, Ying; He, Xuming
  作者单位：Columbia University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Growth charts are often more informative when they are customized per subject, taking into account prior measurements and possibly other covariates of the subject. We study a global semiparametric quantile regression model that has the ability to estimate conditional quantiles without the usual distributional assumptions. The model can be estimated from longitudinal reference data with irregular measurement times and with some level of robustness against outliers, and it is also flexible for i...
• Optimal adaptive estimation of a quadratic functional AI翻译
  作者：Cai, T. Tony; Low, Mark G.
  作者单位：University of Pennsylvania
  摘要：Adaptive estimation of a quadratic functional over both Besov and L(p) balls is considered. A collection of nonquadratic estimators are developed which have useful bias and variance properties over individual Besov and L(p) balls. An adaptive procedure is then constructed based on penalized maximization over this collection of nonquadratic estimators. This procedure is shown to be optimally rate adaptive over the entire range of Besov and L(p) balls in the sense that it attains certain constra...
• Discussion: Conditional growth charts AI翻译
  作者：Carroll, Raymond J.; Ruppert, David
  作者单位：Texas A&M University System; Texas A&M University College Station; Cornell University
• Component selection and smoothing in multivariate nonparametric regression AI翻译
  作者：Lin, Yi; Zhang, Hao Helen
  作者单位：University of Wisconsin System; University of Wisconsin Madison; North Carolina State University
  摘要：We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The COSSO is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified framework for several recent proposals for model selection in linear models and smoothing spline ANOVA models. Theoretical properti...
• Discussion: Conditional growth charts AI翻译
  作者：Thompson, Mary Lou
  作者单位：University of Washington; University of Washington Seattle
• Blocked regular fractional factorial designs with minimum aberration AI翻译
  作者：Xu, Hongquan
  作者单位：University of California System; University of California Los Angeles
  摘要：This paper considers the construction of minimum aberration (MA) blocked factorial designs. Based on coding theory, the concept of minimum moment aberration due to Xu [Statist. Sinica 13 (2003) 691-708] for unblocked designs is extended to blocked designs. The coding theory approach studies designs in a row-wise fashion and therefore links blocked designs with nonregular and supersaturated designs. A lower bound on blocked wordlength pattern is established. It is shown that a blocked design ha...