2002

最新文章

• Projection estimates of multivariate location AI翻译
  作者：Adrover, J; Yohai, V
  作者单位：National University of Cordoba; University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
  摘要：In this paper we study the maximum asymptotic bias of the projection estimate for multivariate location based on univariate estimates of location and dispersion. In particular we study the projection estimate that uses the median and median absolute deviation about the median (MAD) as univariate location and dispersion estimates respectively. This estimator may be considered a natural affine equivariant multivariate median. For spherical distributions the maximum bias of this estimate depends ...
• What is a statistical model? AI翻译
  作者：McCullagh, P
  作者单位：University of Chicago
  摘要：This paper addresses two closely related questions, What is a statistical model? and What is a parameter? The notions that a model must make sense, and that a parameter must have a well-defined meaning are deeply ingrained in applied statistical work, reasonably well understood at an instinctive level, but absent from most formal theories of modelling and inference. In this paper, these concepts are defined in algebraic terms, using morphisms, functors and natural transformations. It is argued...
• Laplace approximations for hypergeometric functions with matrix argument AI翻译
  作者：Butler, RW; Wood, ATA
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Nottingham
  摘要：In this paper we present Laplace approximations for two functions of matrix argument: the Type I confluent hypergeometric function and the Gauss hypergeometric function, Both of these functions play an important role in distribution theory in multivariate analysis, but from a practical point of view they have proved challenging, and they have acquired a reputation for being difficult to approximate, Appealing features of the approximations we present are: (i) they are fully explicit (and simpl...
• Blocked regular fractional factorial designs with maximum estimation capacity (vol 29, pg 530, 2001) AI翻译
  作者：Cheng, CS; Mukerjee, R
  作者单位：University of California System; University of California Berkeley; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta
• 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...
• Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates AI翻译
  作者：Yang, YH; Zhu, D
  作者单位：Iowa State University; Iowa State University
  摘要：We study a multi-armed bandit problem in a setting where covariates are available. We take a nonparametric approach to estimate the functional relationship between the response (reward) and the covariates. The estimated relationships and appropriate randomization are used to select a good arm to play for a greater expected reward. Randomization helps balance the tendency to trust the currently most promising arm with further exploration of other arms, It is shown that, with some familiar nonpa...
• On depth and deep points: A calculus AI翻译
  作者：Mizera, I
  作者单位：University of Alberta; Comenius University Bratislava
  摘要：For a general definition of depth in data analysis a differential-like calculus is constructed in which the location case (the framework of Tukey's median) plays a fundamental role similar to that of linear functions in the mathematical analysis. As an application, a lower bound for maximal regression depth is proved in the general multidimensional case-as conjectured by Rousseeuw and Hubert and others. This lower bound is demonstrated to have an impact on the breakdown point of the maximum de...
• What is a statistical model? Discussion AI翻译
  作者：Fraser, DAS; Reid, N
  作者单位：University of Toronto
• On local likelihood density estimation AI翻译
  作者：Park, BU; Kim, WC; Jones, MC
  作者单位：Seoul National University (SNU); Open University - UK
  摘要：This paper considers a class of local likelihood methods introduced by Eguchi and Copas. Unified asymptotic results are presented in the usual smoothing context of the bandwidth, h, tending to zero as the sample size tends to infinity. We present our results pointwise in the univariate case, but then go on to extend them to global properties and to indicate how to cope with the multivariate case. Specific members of the class due to Copas, and Hjort and Jones are seen to be members of a subset...