2002

最新文章

• Modeling through group invariance: An interesting example with potential applications AI翻译
  作者：Li, H
  作者单位：University of Rochester
  摘要：A particular linear group symmetry model, called the dyadic symmetry model, is studied in some detail. Statistical procedures analogous to (multivariate) analysis of variance are introduced. This model may be suitable for various kinds of data collected on pairs of sampling units. Examples include (complete) diallel cross experiments in genetics and social relations analysis in psychology, for which ad hoc methods of analysis have been developed independently in those disciplines. Our approach...
• Asymptotic nonequivalence of GARCH models and diffusions AI翻译
  作者：Wang, YZ
  作者单位：University of Connecticut
  摘要：This paper investigates the statistical relationship of the LARCH model and its diffusion limit. Regarding the two types of models as two statistical experiments formed by discrete observations from the models, we study their asymptotic equivalence in terms of Le Cam's deficiency distance. To our surprise, we are able to show that the LARCH model and its diffusion limit are asymptotically equivalent only under deterministic volatility. With stochastic volatility, due to the difference between ...
• 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...
• A unified jackknife theory for empirical best prediction with M-estimation AI翻译
  作者：Jiang, JM; Lahiri, P; Wan, SM
  作者单位：University of California System; University of California Davis; University System of Maryland; University of Maryland College Park
  摘要：The paper presents a unified jackknife theory for a fairly general class of mixed models which includes some of the widely used mixed linear models and generalized linear mixed models as special cases. The paper develops jackknife theory for the important, but so far neglected, prediction problem for the general mixed model. For estimation of fixed parameters, a jackknife method is considered for a general class of M-estimators which includes the maximum likelihood, residual maximum likelihood...
• 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.
• What is a statistical model? Discussion AI翻译
  作者：Bickel, PJ
  作者单位：University of California System; University of California Berkeley
• Empirical margin distributions and bounding the generalization error of combined classifiers AI翻译
  作者：Koltchinskii, V; Panchenko, D
  作者单位：University of New Mexico
  摘要：We prove new probabilistic upper bounds on generalization error of complex classifiers that are combinations of simple classifiers. Such combinations could be implemented by neural networks or by voting methods of combining the classifiers, such as boosting and bagging. The bounds are in terms of the empirical distribution of the margin of the combined classifier. They are based on the methods of the theory of Gaussian and empirical processes (comparison inequalities, symmetrization method, co...
• John W. Tukey as philosopher AI翻译
  作者：Dempster, AP
  作者单位：Harvard University
  摘要：Although not a traditional philosopher, John Tukey contributed much to our understanding of statistical science and empirical science more broadly. The former is represented by the light he shed on the relation of drawing conclusions to making decisions, and of how simple concepts like significance and confidence serve to back up or confirm empirical findings. Less successfully, he attempted inconclusively to sort out the ambiguities of R. A. Fisher's fiducial argument. His main effort, howeve...
• The statistical work of Lucien Le Cam AI翻译
  作者：van der Vaart, A
  作者单位：Vrije Universiteit Amsterdam
  摘要：We give an overview and appraisal of the scientific work in theoretical statistics, and its impact, by Lucien Le Cam. The references to Le Cam's papers refer to the Le Cam bibliography. The reference is the first paper for the given year if not stated.
• Random rates in anisotropic regression - Discussion AI翻译
  作者：Brown, LD; Lin, Y
  作者单位：University of Pennsylvania