The Annals of Statistics

最新文章

• Limit theory for the sample autocorrelations and extremes of a GARCH (1,1) process AI翻译
  作者：Mikosch, T; Starica, C
  作者单位：University of Groningen; Chalmers University of Technology
  摘要：The asymptotic theory for the sample autocorrelations and extremes of a GARCH(I, 1) process is provided. Special attention is given to the case when the sum of the ARCH and GARCH parameters is close to 1, that is, when one is close to an infinite Variance marginal distribution. This situation has been observed for various financial log-return series and led to the introduction of the IGARCH model. In such a situation, the sample autocorrelations are unreliable estimators of their deterministic...
• Additive logistic regression: A statistical view of boosting - Discussion AI翻译
  作者：Breiman, L
  作者单位：University of California System; University of California Berkeley
• Efficient estimation of a density in a problem of tomography AI翻译
  作者：Cavalier, L
  作者单位：Aix-Marseille Universite
  摘要：The aim of tomography is to reconstruct a multidimensional function From observations of its integrals over hyperplanes. We consider the model that corresponds to the case of positron emission tomography. We have n i.i.d. observations from a probability density proportional to Rf, where Rf stands for the Radon transform of the density f. We assume that f is an N-dimensional density such that its Fourier transform is exponentially decreasing. We find an estimator of f which is asymptotically ef...
• A likelihood approximation for locally stationary processes AI翻译
  作者：Dahlhaus, R
  作者单位：Ruprecht Karls University Heidelberg
  摘要：A new approximation to the Gaussian likelihood of a multivariate locally stationary process is introduced. It is based on an approximation of the inverse of the covariance matrix of such processes. The new quasi likelihood is a generalization of the classical Whittle likelihood for stationary processes. Several approximation results are proved for the likelihood function. For parametric models, asymptotic normality and efficiency of the resulting estimator are derived for Gaussian locally stat...
• The problem of low counts in a signal plus noise model AI翻译
  作者：Woodroofe, M; Wang, HY
  作者单位：University of Michigan System; University of Michigan; Academia Sinica - Taiwan
  摘要：Consider the model X = B + S, where B and S are independent Poisson random variables with means mu and upsilon, upsilon is unknown, but mu is known. The model arises in particle physics and some recent articles have suggested conditioning on the observed bound on B; that is, if X = n is observed, then the suggestion is to base inference on the conditional distribution of X given B less than or equal to n. This conditioning is non-standard in that it does not correspond to a partition of the sa...
• Data sharpening methods for bias reduction in nonparametric regression AI翻译
  作者：Choi, E; Hall, P; Rousson, V
  作者单位：Australian National University
  摘要：We consider methods for kernel regression when the explanatory and/or response variables are adjusted prior to substitution into a conventional estimator. This data-sharpening procedure is designed to preserve the advantages of relatively simple, low-order techniques, for example, their robustness against design sparsity problems, yet attain the sorts of bias reductions that are commonly associated only with high-order methods. We consider Nadaraya-Watson and local-linear methods in detail, al...
• On the uniqueness of S-functionals and M-functionals under nonelliptical distributions AI翻译
  作者：Tatsuoka, KS; Tyler, DE
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：The S-functionals of multivariate location and scatter, including the MVE-functionals, are known to be uniquely defined only at unimodal elliptically symmetric distributions. The goal of this paper is to establish the uniqueness of these functionals under broader classes of symmetric distributions. We also discuss some implications of the uniqueness of the functionals and give examples of striclty unimodal and symmetric distributions for which the MVE-functional is not uniquely defined. The un...
• Recursive estimation of a drifted autoregressive parameter AI翻译
  作者：Belitser, E
  作者单位：Leiden University - Excl LUMC; Leiden University
  摘要：Suppose the X-0, ..., X-n are observations of a one-dimensional stochastic dynamic process described by autoregression equations when the autoregressive parameter is drifted with time, i.e. it is some function of time: theta (0), ..., theta (n), with theta (k) = theta (k/n). The function theta (t) is assumed to belong a priori to a predetermined nonparametric class of functions satisfying the Lipschitz smoothness condition. At each time point t those observations are accessible which have been...
• Asymptotic approximations for error probabilities of sequential or fixed sample size tests in exponential families AI翻译
  作者：Chan, HP; Lai, TL
  作者单位：National University of Singapore; Stanford University
  摘要：Asymptotic approximations for the error probabilities of sequential tests of composite hypotheses in multiparameter exponential families are developed herein for a general class of test statistics, including generalized likelihood ratio statistics and other functions of the sufficient statistics. These results not only generalize previous approximations for Type I error probabilities of sequential generalized likelihood ratio tests, but also provide a unified treatment of both sequential and f...
• Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV AI翻译
  作者：Lin, XW; Wahba, G; Xiang, D; Gao, FY; Klein, R; Klein, B
  作者单位：University of Wisconsin System; University of Wisconsin Madison; SAS Institute Inc; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：We propose the randomized Generalized Approximate Cross Validation (ranGACV) method for choosing multiple smoothing parameters in penalized likelihood estimates for Bernoulli data. The method is intended for application with penalized likelihood smoothing spline ANOVA models. In addition we propose a class of approximate numerical methods for solving the penalized likelihood variational problem which, in conjunction with the ranGACV method allows the application of smoothing spline ANOVA model...