4期

最新文章

• BANDWIDTH SELECTION IN KERNEL EMPIRICAL RISK MINIMIZATION VIA THE GRADIENT AI翻译
  作者：Chichignoud, Michael; Loustau, Sebastien
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; Universite d'Angers
  摘要：In this paper, we deal with the data-driven selection of multidimensional and possibly anisotropic bandwidths in the general framework of kernel empirical risk minimization. We propose a universal selection rule, which leads to optimal adaptive results in a large variety of statistical models such as nonparametric robust regression and statistical learning with errors in variables. These results are stated in the context of smooth loss functions, where the gradient of the risk appears as a goo...
• ROBUSTNESS TO OUTLIERS IN LOCATION-SCALE PARAMETER MODEL USING LOG-REGULARLY VARYING DISTRIBUTIONS AI翻译
  作者：Desgagne, Alain
  作者单位：University of Quebec; University of Quebec Montreal
  摘要：Estimating the location and scale parameters is common in statistics, using, for instance, the well-known sample mean and standard deviation. However, inference can be contaminated by the presence of outliers if modeling is done with light-tailed distributions such as the normal distribution. In this paper, we study robustness to outliers in location-scale parameter models using both the Bayesian and frequentist approaches. We find sufficient conditions (e.g., on tail behavior of the model) to...
• MAXIMIN EFFECTS IN INHOMOGENEOUS LARGE-SCALE DATA AI翻译
  作者：Meinshausen, Nicolai; Buehlmann, Peter
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Large-scale data are often characterized by some degree of inhomogeneity as data are either recorded in different time regimes or taken from multiple sources. We look at regression models and the effect of randomly changing coefficients, where the change is either smoothly in time or some other dimension or even without any such structure. Fitting varying-coefficient models or mixture models can be appropriate solutions but are computationally very demanding and often return more information t...
• REJOINDER TO DISCUSSIONS OF FREQUENTIST COVERAGE OF ADAPTIVE NONPARAMETRIC BAYESIAN CREDIBLE SETS AI翻译
  作者：Szabo, Botond; van der Vaart, A. W.; van Zanten, J. H.
  作者单位：Eindhoven University of Technology; Leiden University; Leiden University - Excl LUMC; University of Amsterdam
• DISCUSSION OF FREQUENTIST COVERAGE OF ADAPTIVE NONPARAMETRIC BAYESIAN CREDIBLE SETS AI翻译
  作者：Rousseau, Judith
  作者单位：Universite PSL; Universite Paris-Dauphine; Institut Polytechnique de Paris; ENSAE Paris
• DISCUSSION OF FREQUENTIST COVERAGE OF ADAPTIVE NONPARAMETRIC BAYESIAN CREDIBLE SETS AI翻译
  作者：Low, Mark G.; Ma, Zongming
  作者单位：University of Pennsylvania
• JUMP ACTIVITY ESTIMATION FOR PURE-JUMP SEMIMARTINGALES VIA SELF-NORMALIZED STATISTICS AI翻译
  作者：Todorov, Viktor
  作者单位：Northwestern University
  摘要：We derive a nonparametric estimator of the jump-activity index beta of a locally-stable pure-jump Ito semimartingale from discrete observations of the process on a fixed time interval with mesh of the observation grid shrinking to zero. The estimator is based on the empirical characteristic function of the increments of the process scaled by local power variations formed from blocks of increments spanning shrinking time intervals preceding the increments to be scaled. The scaling serves two pu...
• DISCUSSION OF FREQUENTIST COVERAGE OF ADAPTIVE NONPARAMETRIC BAYESIAN CREDIBLE SETS AI翻译
  作者：Ghosal, Subhashis
  作者单位：North Carolina State University
• FREQUENTIST COVERAGE OF ADAPTIVE NONPARAMETRIC BAYESIAN CREDIBLE SETS AI翻译
  作者：Szabo, Botond; van der Vaart, A. W.; van Zanten, J. H.
  作者单位：Eindhoven University of Technology; Leiden University - Excl LUMC; Leiden University; University of Amsterdam
  摘要：We investigate the frequentist coverage of Bayesian credible sets in a nonparametric setting. We consider a scale of priors of varying regularity and choose the regularity by an empirical Bayes method. Next we consider a central set of prescribed posterior probability in the posterior distribution of the chosen regularity. We show that such an adaptive Bayes credible set gives correct uncertainty quantification of polished tail parameters, in the sense of high probability of coverage of such p...
• SEMIPARAMETRIC GEE ANALYSIS IN PARTIALLY LINEAR SINGLE-INDEX MODELS FOR LONGITUDINAL DATA AI翻译
  作者：Chen, Jia; Li, Degui; Liang, Hua; Wang, Suojin
  作者单位：University of York - UK; University of York - UK; George Washington University; Texas A&M University System; Texas A&M University College Station
  摘要：In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link function. Under some mild conditions, we derive the asymptotic properties of the proposed parametric and nonp...