2期

最新文章

• Generalized martingale-residual processes for goodness-of-fit inference in Cox's type regression models AI翻译
  作者：Marzec, L; Marzec, P
  作者单位：University of Wroclaw
  摘要：In the paper a general class of stochastic processes based on the sums of weighted martingale-transform residuals for goodness-of-fit inference in general Cox's type regression models is studied. Their form makes the inference robust to covariate outliers. A weak convergence result for such processes is obtained giving the possibility of establishing the randomness of their graphs together with the construction of the formal chi(2)-type goodness-of-fit tests. By using the Khmaladze innovation ...
• On average derivative quantile regression AI翻译
  作者：Chaudhuri, P; Doksum, K; Samarov, A
  作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; University of California System; University of California Berkeley; Massachusetts Institute of Technology (MIT)
  摘要：For fixed alpha epsilon (0, 1), the quantile regression function gives the alpha th quantile theta(alpha)(x) in the conditional distribution of a response variable Y given the value X = x of a vector of covariates. It can be used to measure the effect of covariates not only in the center of a population, but also in the upper and lower tails. A functional that summarizes key features of the quantile specific relationship between X and Y is the vector beta(alpha) of weighted expected values of ...
• Curve estimation when the design density is low AI翻译
  作者：Hall, P; Marron, JS; Neumann, MH; Titterington, DM
  作者单位：Australian National University
  摘要：in problems where a high-dimensional design is projected into a lower number of dimensions, the density of the new design is typically not bounded away from zero over its support, even if the original one was. Contexts where this problem arises include projection pursuit regression, estimation in single index models and application of the projection-slice method of Radon transform inversion. Theoretical work in these settings typically involves ignoring data toward the ends of the support of t...
• Generalization of likelihood ratio tests under nonstandard conditions AI翻译
  作者：Vu, HTV; Zhou, S
  作者单位：University of Western Australia
  摘要：In this paper, we analyze the statistic which is the difference in the values of an estimating function evaluated at its local maxima on two different subsets of the parameter space, assuming that the true parameter is in each subset, but possibly on the boundary. Our results extend known methods by covering a large class of estimation problems which allow sampling from nonidentically distributed random variables. Specifically, the existence and consistency of the local maximum estimators and ...
• Multidimensional medians arising from geodesics on graphs AI翻译
  作者：Small, CG
  作者单位：University of Waterloo
  摘要：In this paper we introduce a depth function for distributions on graphs that is analogous to recent multivariate definitions. Using the property of geodesic convexity on graphs, a median-like center for distributions on graphs is constructed and applied to ranking data as well as multivariate data spanned by the minimal spanning tree.
• An Edgeworth expansion for symmetric statistics AI翻译
  作者：Bentkus, V; Götze, F; van Zwet, WR
  作者单位：University of Bielefeld; Leiden University; Leiden University - Excl LUMC; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We consider asymptotically normal statistics which are symmetric functions of N i.i.d. random variables. For these statistics we prove the validity of an Edgeworth expansion with remainder O(N-1) under Cramer's condition on the linear part of the statistic and moment assumptions for all parts of the statistic. By means of a counterexample we show that it is generally not possible to obtain an Edgeworth expansion with remainder o(N-1) without imposing additional assumptions on the structure of ...
• Trimmed k-means:: An attempt to robustify quantizers AI翻译
  作者：Cuesta-Albertos, JA; Gordaliza, A; Matrán, C
  作者单位：Universidad de Cantabria; Universidad de Valladolid
  摘要：A class of procedures based on impartial trimming (self-determined by the data) is introduced with the aim of robustifying k-means, hence the associated clustering analysis. We include a detailed study of optimal regions, showing that only nonpathological regions can arise from impartial trimming procedures. The asymptotic results provided in the paper focus on strong consistency of the suggested methods under widely general conditions. A section is devoted to exploring the performance of the ...
• Adaptive estimation in time-series models AI翻译
  作者：Drost, FC; Klaassen, CAJ; Werker, BJM
  作者单位：Tilburg University; Tilburg University; University of Amsterdam
  摘要：In a framework particularly suited for many time-series models we obtain a LAN result under quite natural and economical conditions. This enables us to construct adaptive estimators for (part of) the Euclidean parameter in these semiparametric models. Special attention is directed to group models in time series with the important subclass of models with time varying location and scale. Our set-up is confronted with the existing literature and, as examples, we reconsider linear regression and A...
• Convergence of depth contours for multivariate datasets AI翻译
  作者：He, XM; Wang, G
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; DePaul University
  摘要：Contours of depth often provide a good geometrical understanding of the structure of a multivariate dataset. They are also useful in robust statistics in connection with generalized medians and data ordering. If the data constitute a random sample from a spherical or elliptic distribution, the depth contours are generally required to converge to spherical or elliptical shapes. We consider contour constructions based on a notion of data depth and prove a uniform contour convergence theorem unde...