Journal of the Royal Statistical Society: Series B

最新文章

• Estimation of the common mean from heterogeneous normal observations with unknown variances AI翻译
  作者：Rukhin, Andrew L.
  作者单位：National Institute of Standards & Technology (NIST) - USA
  摘要：To determine the common mean of heterogeneous normal observations, the Bayes procedures and the invariant maximum likelihood estimators of the weights forming the weighted means statistic are obtained when there are no variance estimates. The Bayes statistic is based on the reference, Geisser-Cornfield prior distribution which makes the posterior (discrete) distribution of the mean to be supported by the observed data with probabilities determined via the geometric means of the distances betwe...
• The p-filter: multilayer false discovery rate control for grouped hypotheses AI翻译
  作者：Barber, Rina Foygel; Ramdas, Aaditya
  作者单位：University of Chicago; University of California System; University of California Berkeley
  摘要：In many practical applications of multiple testing, there are natural ways to partition the hypotheses into groups by using the structural, spatial or temporal relatedness of the hypotheses, and this prior knowledge is not used in the classical Benjamini-Hochberg procedure for controlling the false discovery rate (FDR). When one can define (possibly several) such partitions, it may be desirable to control the group FDR simultaneously for all partitions (as special cases, the 'finest' partition...
• Oracally efficient estimation and consistent model selection for auto-regressive moving average time series with trend AI翻译
  作者：Shao, Qin; Yang, Lijian
  作者单位：Soochow University - China; University System of Ohio; University of Toledo; Tsinghua University
  摘要：Most time series that are encountered in practice contain non-zero trend, yet textbook approaches to time series analysis are typically focused on zero-mean stationary auto-regressive moving average (ARMA) processes. Trend is often estimated by ad hoc methods and subtracted from time series, and the residuals are used as the true ARMA noise for data analysis and inference, including parameter estimation, lag selection and prediction. We propose a theoretically justified two-step method to anal...
• Sparse graphs using exchangeable random measures AI翻译
  作者：Caron, Francois; Fox, Emily B.
  作者单位：University of Oxford; University of Washington; University of Washington Seattle
  摘要：Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this arraywhich can aid in modelling, computations and theoretical analysisthe Aldous-Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and appeal to the Kallenberg representation theorem for this object. We explore using completely rand...
• Local explosion modelling by non-causal process AI翻译
  作者：Gourieroux, Christian; Zakoian, Jean-Michel
  作者单位：Institut Polytechnique de Paris; ENSAE Paris; University of Toronto; Universite de Lille
  摘要：The non-causal auto-regressive process with heavy-tailed errors has non-linear causal dynamics, which allow for local explosion or asymmetric cycles that are often observed in economic and financial time series. It provides a new model for multiple local explosions in a strictly stationary framework. The causal predictive distribution displays surprising features, such as higher moments than for the marginal distribution, or the presence of a unit root in the Cauchy case. Aggregating such mode...
• Continuous auto-regressive moving average random fields on n AI翻译
  作者：Brockwell, Peter J.; Matsuda, Yasumasa
  作者单位：Colorado State University System; Colorado State University Fort Collins; Tohoku University
  摘要：We define an isotropic Levy-driven continuous auto-regressive moving average CARMA(p,q) random field on Rn as the integral of a radial CARMA kernel with respect to a Levy sheet. Such fields constitute a parametric family characterized by an auto-regressive polynomial a and a moving average polynomial b having zeros in both the left and the right complex half-planes. They extend the well-balanced Ornstein-Uhlenbeck process of Schnurr and Woerner to a well-balanced CARMA process in one dimension...
• Modelling across extremal dependence classes AI翻译
  作者：Wadsworth, J. L.; Tawn, J. A.; Davison, A. C.; Elton, D. M.
  作者单位：Lancaster University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available statistical models suit one or other of these cases, but not both, resulting in a stage in the inference that is unaccounted for but can substantially impact subsequent extrapolation. Existing modelling solutions to this problem are either appli...
• Laplace deconvolution on the basis of time domain data and its application to dynamic contrast-enhanced imaging AI翻译
  作者：Comte, Fabienne; Cuenod, Charles-A.; Pensky, Marianna; Rozenholc, Yves
  作者单位：Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Universite Paris Cite; Hopital Universitaire Europeen Georges-Pompidou - APHP; State University System of Florida; University of Central Florida
  摘要：We consider the problem of Laplace deconvolution with noisy discrete non-equally spaced observations on a finite time interval. We propose a new method for Laplace deconvolution which is based on expansions of the convolution kernel, the unknown function and the observed signal over a Laguerre functions basis (which acts as a surrogate eigenfunction basis of the Laplace convolution operator) using a regression setting. The expansion results in a small system of linear equations with the matrix...
• Random-projection ensemble classification AI翻译
  作者：Cannings, Timothy I.; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：We introduce a very general method for high dimensional classification, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower dimensional space. In one special case that we study in detail, the random projections are divided into disjoint groups, and within each group we select the projection yielding the smallest estimate of the test error. Our random-projection ensemble classifier then aggregates the res...
• Maximum likelihood estimation for linear Gaussian covariance models AI翻译
  作者：Zwiernik, Piotr; Uhler, Caroline; Richards, Donald
  作者单位：Pompeu Fabra University; Massachusetts Institute of Technology (MIT); Institute of Science & Technology - Austria; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian models with linear constraints on the covariance matrix. Maximum likelihood estimation for this class of models leads to a non-convex optimization problem which typically has many local maxima. Using recent results on the asymptotic distribution of extreme eigenvalues of the Wishart distribution, we provide sufficient conditions for any hill climbing method to converge to the global maximum. Al...