2004

最新文章

• An adaptation theory for nonparametric confidence intervals AI翻译
  作者：Cai, TT; Low, MG
  作者单位：University of Pennsylvania
  摘要：A nonparametric adaptation theory is developed for the construction of confidence intervals for linear functionals. A between class modulus of continuity captures the expected length of adaptive confidence intervals. Sharp lower bounds are given for the expected length and an ordered modulus of continuity is used to construct adaptive confidence procedures which are within a constant factor of the lower bounds. In addition, minimax theory over nonconvex parameter spaces is developed.
• Bump hunting with non-Gaussian kernels AI翻译
  作者：Hall, P; Minnotte, MC; Zhang, CM
  作者单位：Australian National University; Utah System of Higher Education; Utah State University; University of Wisconsin System; University of Wisconsin Madison
  摘要：It is well known that the number of modes of a kernel density estimator is monotone nonincreasing in the bandwidth if the kernel is a Gaussian density. There is numerical evidence of nonmonotonicity in the case of some non-Gaussian kernels, but little additional information is available. The present paper provides theoretical and numerical descriptions of the extent to which the number of modes is a nonmonotone function of bandwidth in the case of general compactly supported densities. Our res...
• New approaches to Bayesian consistency AI翻译
  作者：Walker, S
  作者单位：University of Bath
  摘要：We use martingales to study Bayesian consistency. We derive sufficient conditions for both Hettinger and Kullback-Leibler consistency, which do not rely on the use of a sieve. Alternative sufficient conditions for Hellinger consistency are also found and demonstrated on examples.
• Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory AI翻译
  作者：Grünwald, PD; Dawid, AP
  作者单位：Centrum Wiskunde & Informatica (CWI); University of London; University College London
  摘要：We describe and develop a close relationship between two problems that have customarily been regarded as distinct: that of maximizing entropy, and that of minimizing worst-case expected loss. Using a formulation grounded in the equilibrium theory of zero-sum games between Decision Maker and Nature, these two problems are shown to be dual to each other, the solution to each providing that to the other. Although Topsoe described this connection for the Shannon entropy over 20 years ago, it does ...
• Least angle regression - Discussion AI翻译
  作者：Loubes, JM; Massart, P
  作者单位：Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
• Hybrid shrinkage estimators using penalty bases for the ordinal one-way layout AI翻译
  作者：Beran, R
  作者单位：University of California System; University of California Davis
  摘要：This paper constructs improved estimators of the means in the Gaussian saturated one-way layout with an ordinal factor. The least squares estimator for the mean vector in this saturated model is usually inadmissible. The hybrid shrinkage estimators of this paper exploit the possibility of slow variation in the dependence of the means on the ordered factor levels but do not assume it and respond well to faster variation if present. To motivate the development, candidate penalized least squares ...
• Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime AI翻译
  作者：Douc, R; Moulines, T; Rydén, T
  作者单位：IMT - Institut Mines-Telecom; IMT Atlantique; Lund University
  摘要：An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution...
• Mean squared error of empirical predictor AI翻译
  作者：Das, K; Jiang, JM; Rao, JNK
  作者单位：University of Calcutta; Carleton University; University of California System; University of California Davis
  摘要：The term empirical predictor refers to a two-stage predictor of a linear combination of fixed and random effects. In the first stage, a predictor is obtained but it involves unknown parameters; thus, in the second stage, the unknown parameters are replaced by their estimators. In this paper, we consider mean squared errors (MSE) of empirical predictors under a general setup, where ML or REML estimators are used for the second stage. We obtain second-order approximation to the MSE as well as an...
• Nonparametric estimation of an additive model with a link function AI翻译
  作者：Horowitz, JL; Mammen, E
  作者单位：Northwestern University; University of Mannheim
  摘要：This paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically normally distributed with a rate of convergence in probability of n(-2/5). This is true regardless of the (finite) dimension of the explanatory variable. Thus, in contrast to the existing asymptotically normal estimator, the new estimator has no curse of dimensionality. More...
• Saddlepoint approximation for Student's t-statistic with no moment conditions AI翻译
  作者：Jing, BY; Shao, QM; Zhou, W
  作者单位：Hong Kong University of Science & Technology; University of Oregon; National University of Singapore
  摘要：A saddlepoint approximation of the Student's t-statistic was derived by Daniels and Young [Biometrika 78 (1991) 169-179] under the very stringent exponential moment condition that requires that the underlying density function go down at least as fast as a Normal density in the tails. This is a severe restriction on the approximation's applicability. In this paper we show that this strong exponential moment restriction can be completely dispensed with, that is, saddlepoint approximation of the ...