The Annals of Statistics

最新文章

• A nonparametric calibration analysis AI翻译
  作者：Gruet, MA
  摘要：In this paper we discuss a new approach to solve calibration problems in a nonparametric setting. This approach is appealing because it fields estimates of the required quantities directly. The method combines kernel and robust estimation techniques. It relies on strong approximations of the estimating process and the extreme value theorem of Bickel and Rosenblatt. Using these results. we first obtain robust pointwise estimates of the parameters of interest. Second. Re set up asymptotic simult...
• Least upper bound for the covariance matrix of a generalized least squares estimator in regression with applications to a seemingly unrelated regression model and a heteroscedastic model AI翻译
  作者：Kurata, H; Kariya, T
  作者单位：Hitotsubashi University
  摘要：In a general normal regression model, this paper first derives the least upper bound (LUB) for the covariance matrix of a generalized least squares estimator (GLSE) relative to the covariance matrix of the Gauss-Markov estimator. Second the result is applied to the (unrestricted) Zellner estimator in an N-equation seemingly unrelated regression (SUR) model and to the GLSE in a heteroscedastic model.
• Robust inference for variance components models for single trees of cell lineage data AI翻译
  作者：Huggins, RM
  摘要：Previously, Huggins and Staudte examined robust estimators for a variance components formulation of the bifurcating autoregressive model for cell lineage data. They gave asymptotic properties of the estimators ifa large number of trees were observed. However, for single trees the derivation of these asymptotic properties is more complex. Here the asymptotic distributions of robust estimators of parameters associated with the stationary bifurcating autoregressive process as a single tree become...
• An empirical Bayes approach to directional data and efficient computation on the sphere AI翻译
  作者：Healy, DM; Kim, PT
  作者单位：University of Guelph
  摘要：This paper proposes a consistent nonparametric empirical Bayes estimator of the prior density for directional data. The methodology is to use Fourier analysis on S-2 to adapt Euclidean techniques to this non-Euclidean environment. General consistency results are obtained. In addition, a discussion of efficient numerical computation of Fourier transforms on S-2 is given, and their applications to the methods suggested in this paper are sketched.
• Smoothed functional principal, components analysis by choice of norm AI翻译
  作者：Silverman, BW
  摘要：The principal components analysis of functional data is often enhanced by the use of smoothing. It is shown that an attractive method of incorporating smoothing is to replace the usual L(2)-orthonormality constraint on the principal components by orthonormality with respect to an inner product that takes account of the roughness of the functions. The method is easily implemented in practice by making use of appropriate function transforms (Fourier transforms for periodic data) and standard pri...
• A universally acceptable smoothing factor for kernel density estimates AI翻译
  作者：Devroye, L; Lugosi, G
  作者单位：Budapest University of Technology & Economics
  摘要：We define a minimum distance estimate of the smoothing factor for kernel density estimates, based on a methodology first developed by Yatracos. It is shown that if f(nh) denotes the kernel density estimate on R(d) for an i.i.d. sample of size n drawn from an unknown density f, where h is the smoothing factor, and if f(n) is the kernel estimate with the same kernel and with the proposed new data-based smoothing factor, then, under a regularity condition on the kernel K, sup lim sup/fn-->infinit...
• Robust estimation in structured linear regression AI翻译
  作者：Mili, L; Coakley, CW
  作者单位：Virginia Polytechnic Institute & State University
  摘要：A structured linear regression model is one in which there are permanent dependencies among some p row vectors of the n x p design matrix. To study structured linear regression, we introduce a new class of robust estimators, called D-estimators, which can be regarded as a generalization of the least median of squares and least trimmed squares estimators. They minimize a dispersion function of the ordered absolute residuals up to the rank h. We investigate their breakdown point and exact fit po...
• Optimal stopping of sequential size-dependent search AI翻译
  作者：FakhreZakeri, I; Slud, E
  作者单位：University System of Maryland; University of Maryland College Park
  摘要：In many areas of application, one searches within finite populations for items of interest, where the probability of sampling an item fs proportional to a random size attribute from an i.i.d. superpopulation of attributes which may or may not be observable upon discovery. Here we treat the problem of asymptotically optimal stopping rules for size-dependent searches of this type, as the size of the underlying population grows, where the loss function includes an asymptotically smooth time-depen...
• Dynamic sampling policy for detecting a change in distribution, with a probability bound on false alarm AI翻译
  作者：Yakir, B
  摘要：We show that if dynamic sampling is feasible, then there exist surveillance schemes that satisfy a probability constraint on false alarm. Procedures are suggested for detecting a change of a normal mean from 0 to a (unknown) positive value. These procedures are optimal (up to a constant term) when the post-change mean is known, and almost optimal [up to an o(log(1/alpha)) term] when the post-change mean is unknown.
• Algorithms for computing self-consistent and maximum likelihood estimators with doubly censored data AI翻译
  作者：Mykland, PA; Ren, JJ
  作者单位：University of Nebraska System; University of Nebraska Lincoln
  摘要：The paper investigates the structure of the self-consistent estimators (SCE) and the nonparametric maximum likelihood estimator (NPMLE) for doubly censored data. An explicit sufficient and necessary condition for an SCE to be the NPMLE is given. Based on this, algorithms for computing the SCE and the NPMLE are provided. The relation between our algorithms and the EM algorithm is studied.