The Annals of Statistics

最新文章

• Lattice-based D-optimum design for Fourier regression AI翻译
  作者：Riccomagno, E; Schwabe, R; Wynn, HP
  作者单位：University of Warwick; Technical University of Darmstadt
  摘要：A theory of optimum orthogonal fractions is developed for Fourier regression models using integer lattice designs. These provide alternatives to simple grids (product designs) in the case when specified main effects and interaction terms are required to be analyzed. The challenge is to obtain sample sizes which are polynomial in the dimension rather than exponential. This is achieved for certain models with special algorithms based on both algebraic generation and more direct sequential search.
• Bandit problems with infinitely many arms AI翻译
  作者：Berry, DA; Chen, RW; Zame, A; Heath, DC; Shepp, LA
  作者单位：Duke University; University of Miami; Cornell University; AT&T
  摘要：We consider a bandit problem consisting of a sequence of n choices from an infinite number of Bernoulli arms, with n --> infinity. The objective is to minimize the long-run failure rate. The Bernoulli parameters are independent observations from a distribution F. We first assume F to be the uniform distribution on (0, 1) and consider various extensions. In the uniform case we show that the best lower bound for the expected failure proportion is between root 2/root n and 2/root n and we exhibit...
• Smoothed Cox regression AI翻译
  作者：Dabrowska, DM
  作者单位：University of California System; University of California Los Angeles
  摘要：Nonparametric regression was shown by Beran and McKeague and Utikal to provide a flexible method for analysis of censored failure times and more general counting processes models in the presence of covariates. We discuss application of kernel smoothing towards estimation in a generalized Cox regression model with baseline intensity dependent on a covariate. Under regularity conditions we show that estimates of the regression parameters are asymptotically normal at rate root-n, and we also disc...
• 1994 Wald memorial lectures - Polynomial splines and their tensor products in extended linear modeling - Discussion AI翻译
  作者：Gu, C
  作者单位：Purdue University System; Purdue University
• 1994 Wald memorial lectures - Polynomial splines and their tensor products in extended linear modeling - Discussion AI翻译
  作者：Hastie, T; Tibshirani, R
  作者单位：Stanford University; University of Toronto
• Wald consistency and the method of sieves in REML estimation AI翻译
  作者：Jiang, JM
  作者单位：University System of Ohio; Case Western Reserve University
  摘要：We prove that for all unconfounded balanced mixed models of the analysis of variance, estimates of variance components parameters that maximize the (restricted) Gaussian Likelihood are consistent and asymptotically normal-and this is true whether normality is assumed or not. For a general (nonnormal) mixed model, we show estimates of the variance components parameters that maximize the (restricted) Gaussian likelihood over a sequence of approximating parameter spaces (i.e., a sieve) constitute...
• Optimal discrimination designs for multifactor experiments AI翻译
  作者：Dette, H; Röder, I
  作者单位：Ruhr University Bochum; Leipzig University
  摘要：In this paper efficient designs are determined when Anderson's procedure is applied in order to identify the degree of a multivariate polynomial regression model. It is shown that the optimal designs are very closely related to model robust designs which maximize a weighted p-mean of D-efficiencies. As a consequence we obtain designs with high efficiency for model discrimination and for the statistical analysis in the identified model.
• On the estimation of extreme tail probabilities AI翻译
  作者：Hall, P; Weissman, I
  作者单位：Australian National University; Technion Israel Institute of Technology
  摘要：Applications of extreme value theory to problems of statistical inference typically involve estimating tail probabilities well beyond the range of the data, without the benefit of a concise mathematical model for the sampling distribution. The available model is generally only an asymptotic one. That is, an approximation to probabilities of extreme deviation is supposed, which is assumed to become increasingly accurate as one moves further from the range of the data, but whose concise accuracy...
• 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...
• Semiparametric inference in a partial linear model AI翻译
  作者：Bhattacharya, PK; Zhao, PL
  作者单位：University of California System; University of California Davis; Merck & Company
  摘要：In a partial linear model, the dependence of a response variate Y on covariates (W, X) is given by Y = W beta + eta(X)+ E, where E is independent of (W, X) with densities g and f, respectively. In this paper an asymptotically efficient estimator of beta is constructed solely under mild smoothness assumptions on the unknown eta, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.