471期

最新文章

• Transdimensional Markov chains: A decade of progress and future perspectives AI翻译
  作者：Sisson, SA
  作者单位：University of New South Wales Sydney
  摘要：The last 10 years have witnessed the development of sampling frameworks that permit the construction of Markov chains that simultaneously traverse both parameter and model space. Substantial methodological progress has been made during this period. In this article we present a survey of the current state of the art and evaluate some of the most recent advances in this field. We also discuss future research perspectives in the context of the drive to develop sampling mechanisms with high degree...
• Spike and slab gene selection for multigroup microarray data AI翻译
  作者：Ishwaran, H; Rao, JS
  作者单位：Cleveland Clinic Foundation; University System of Ohio; Case Western Reserve University
  摘要：DNA microarrays can provide insight into genetic changes that characterize different stages of a disease process. Accurate identification of these changes has significant therapeutic and diagnostic implications. Statistical analysis for multistage (multigroup) data is challenging, however. ANOVA-based extensions of two-sample Z-tests, a popular method for detecting differentially expressed genes in two groups, do not work well in multigroup settings. False detection rates are high because of v...
• The generalized dynamic factor model: One-sided estimation and forecasting AI翻译
  作者：Forni, M; Hallin, M; Lippi, M; Reichlin, L
  作者单位：Universita di Modena e Reggio Emilia; Universite Libre de Bruxelles; Universite Libre de Bruxelles; Sapienza University Rome
  摘要：This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic factor model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that i...
• The joint measurement of technical and allocative inefficiencies: An application of Bayesian inference in nonlinear random-effects models AI翻译
  作者：Kumbhakar, SC; Tsionas, EG
  作者单位：State University of New York (SUNY) System; Binghamton University, SUNY; Athens University of Economics & Business
  摘要：This article estimates technical and allocative inefficiencies and increase in costs therefrom of individual firms using a translog cost system consisting of the cost function and the cost share equations. We call it a nonlinear random-effects system because technical and allocative inefficiencies are random (hence the term random effects) and are separated from the random noise terms appearing in each equation of the system, and because the inefficiency terms appear in the system in a highly ...
• The profile sampler AI翻译
  作者：Lee, BL; Kosorok, MR; Fine, JP
  作者单位：National University of Singapore; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：We consider frequentist inference for the parametric component 0 separately from the nuisance parameter eta in semiparametric models based on sampling from the posterior of the profile likelihood. We prove that this procedure gives a first-order-correct approximation to the maximum likelihood estimator 0,, and consistent estimation of the efficient Fisher information for 0, without computing derivatives or using complicated numerical approximations. An exact Bayesian interpretation is establis...
• Semilinear high-dimensional model for normalization of microarray data: A theoretical analysis and partial consistency - Rejoinder AI翻译
  作者：Fan, JQ; Peng, H; Huang, T; Ren, Y
  作者单位：Princeton University; Yale University; University of Hong Kong
• Bayesian nonparametric spatial modeling with Dirichlet process mixing AI翻译
  作者：Gelfand, AE; Kottas, A; MacEachern, SN
  作者单位：Duke University; University of California System; University of California Santa Cruz; University System of Ohio; Ohio State University
  摘要：Customary modeling for continuous point-referenced data assumes a Gaussian process that is often taken to be stationary. When such models are fitted within a Bayesian framework, the unknown parameters of the process are assumed to be random. so a random Gaussian process results. Here we propose a novel spatial Dirichlet process mixture model to produce a random spatial process that is neither Gaussian nor stationary. We first develop a spatial Dirichlet process model for spatial data and discu...
• CATS: Clustering after transformation and smoothing AI翻译
  作者：Serban, N; Wasserman, L
  作者单位：Carnegie Mellon University
  摘要：CATS-clustering after transformation and smoothing-is a technique for nonparametrically estimating and clustering a large number of curves. Our motivating example is a genetic microarray experiment, but the method is very general. The method includes transformation and smoothing multiple curves, multiple nonparametric testing for screening out flat curves, clustering curves with similar shape, and nonparametrically inferring the clustering estimation error rate.