6期

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...
• 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...