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最新文章

• 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...
• Approximate p-values for local sequence alignments AI翻译
  作者：Siegmund, D; Yakir, B
  作者单位：Stanford University; Hebrew University of Jerusalem
  摘要：Assume that two sequences from a finite alphabet are optimally aligned according to a scoring system that rewards similarities according to a general scoring scheme and penalizes gaps (insertions and deletions). Under the assumption that the letters in each sequence are independent and identically distributed and the two sequences are also independent, approximate p-values are obtained for the optimal local alignment when either (i) there are at most a fixed number of gaps, or (ii) the gap ini...
• Additive logistic regression: A statistical view of boosting - Discussion AI翻译
  作者：Freund, Y; Schapire, RE
  作者单位：AT&T
• Bayesian analysis of mixture models with an unknown number of components - An alternative to reversible jump methods AI翻译
  作者：Stephens, M
  作者单位：University of Oxford
  摘要：Richardson and Green present a method of performing a Bayesian analysis of data from a finite mixture distribution with an unknown number of components. Their method is a Markov Chain Monte Carlo (MCMC) approach, which makes use of the reversible jump methodology described by Green. We describe an alternative MCMC method which views the parameters of the model as a (marked point process, extending methods suggested by Ripley to create a Markov birth-death process with an appropriate stationary...