2期

最新文章

• Dimension reduction for conditional mean in regression AI翻译
  作者：Cook, RD; Bing, L
  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
  摘要：In many situations regression analysis is mostly concerned with inferring about the conditional mean of the response given the predictors, and less concerned with the other aspects of the conditional distribution. In this paper we develop dimension reduction methods that incorporate this consideration. We introduce the notion of the Central Mean Subspace (CMS), a natural inferential object for dimension reduction when the mean function is of interest. We study properties of the CMS, and develo...
• Random rates in anisotropic regression -: Discussion AI翻译
  作者：Birgé, L
  作者单位：Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Cite
• Random rates in anisotropic regression - Discussion AI翻译
  作者：Efromovich, S
  作者单位：University of New Mexico
• Sufficient dimension reduction in regressions with categorical predictors AI翻译
  作者：Chiaromonte, F; Cook, RD; Li, B
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
  摘要：In this article, we describe how the theory of sufficient dimension reduction, and a well-known inference method for it (sliced inverse regression), can be extended to regression analyses involving both quantitative and categorical predictor variables. As statistics faces an increasing need for effective analysis strategies for high-dimensional data, the results we present significantly widen the applicative scope of sufficient dimension reduction and open the way for a new class of theoretica...
• Effect of dependence on stochastic measures of accuracy of density estimators AI翻译
  作者：Claeskens, G; Hall, P
  作者单位：Australian National University; Texas A&M University System; Texas A&M University College Station
  摘要：In kernel density estimation, those data values that make a nondegenerate contribution to the estimator (computed at a given point) tend to be spaced well apart. This property has the effect of suppressing many of the conventional consequences of long-range dependence, for example, slower rates of convergence, which might otherwise be revealed by a traditional loss-or risk-based assessment of performance. From that viewpoint, dependence has to be very long-range indeed before a density estimat...
• Random rates in anisotropic regression - Discussion AI翻译
  作者：Picard, D; Tribouley, K
  作者单位：Universite Paris Cite
• Random rates in anisotropic regression - Rejoinder AI翻译
  作者：Hoffmann, M; Lepski, O
  作者单位：Universite Paris Cite; Aix-Marseille Universite; Centre National de la Recherche Scientifique (CNRS)
  摘要：We are grateful to all the participants for their stimulating comments and insightful questions. Reading the notes of the contributers, we progressively came to a better understanding of the imperfections of our approach. It also has given an orientation for further efforts: trying to answer their questions, we found several interesting problems for future work. We also believe and hope that this discussion will be relevant for potential readers since it somehow represents the state of modern ...
• Weak dependence beyond mixing and asymptotics for nonparametric regression AI翻译
  作者：Nze, PA; Bühlmann, P; Doukhan, P
  作者单位：Universite de Lille; Swiss Federal Institutes of Technology Domain; ETH Zurich; CY Cergy Paris Universite
  摘要：We consider a new concept of weak dependence, introduced by Doukhan and Louhichi [Stochastic Process. Appl. 84 (1999) 313-342], which is more general than the classical frameworks of mixing or associated sequences. The new notion is broad enough to include many interesting examples such as very general Bernoulli shifts, Markovian models or time series bootstrap processes with discrete innovations. Under such a weak dependence assumption, we investigate nonparametric regression which represents...
• A class of robust and fully efficient regression estimators AI翻译
  作者：Gervini, D; Yohai, VJ
  作者单位：University of Zurich; University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
  摘要：This paper introduces a new class of robust estimators for the linear regression model. They are weighted least squares estimators, with weights adaptively computed using the empirical distribution of the residuals of an initial robust estimator. It is shown that under certain general conditions the asymptotic breakdown points of the proposed estimators are not less than that of the initial estimator, and the finite sample breakdown point can be at most 1/n less. For the special case of the le...
• Risk bounds in isotonic regression AI翻译
  作者：Zhang, CH
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Nonasymptotic risk bounds are provided for maximum likelihood-type isotonic estimators of an unknown nondecreasing regression function, with general average loss at design points. These bounds are optimal Lip to scale constants. and they imply uniform n(-1/3)-consistency of the l(p) risk for unknown regression functions of uniformly bounded variation, under mild assumptions on the joint probability distribution of the data, with possibly dependent observations.