3期

最新文章

• On the toric algebra of graphical models AI翻译
  作者：Geiger, Dan; Meek, Christopher; Sturmfels, Bernd
  作者单位：Technion Israel Institute of Technology; Microsoft; University of California System; University of California Berkeley
  摘要：We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general exponential models. For decomposable graphical models these conditions are equivalent to a set of conditional independence statements similar to the Hammersley-Clifford theorem; however, we show that for nondecomposable graphical models they are not. We also show that nondecomposable models can hav...
• Bayesian analysis for reversible Markov chains AI翻译
  作者：Diaconis, Persi; Rolles, Silke W. W.
  作者单位：Stanford University; Eindhoven University of Technology
  摘要：We introduce a natural conjugate prior for the transition matrix of a reversible Markov chain. This allows estimation and testing. The prior arises from random walk with reinforcement in the same way the Dirichlet prior arises from Polya's urn. We give closed form normalizing constants, a simple method of simulation from the posterior and a characterization along the lines of W. E. Johnson's characterization of the Dirichlet prior.
• Estimation for almost periodic processes AI翻译
  作者：Lii, Keh-Shin; Rosenblatt, Murray
  作者单位：University of California System; University of California Riverside; University of California System; University of California San Diego
  摘要：Processes with almost periodic covariance functions have spectral mass on lines parallel to the diagonal in the two-dimensional spectral plane. Methods have been given for estimation of spectral mass on the lines of spectral concentration if the locations of the lines are known. Here methods for estimating the intercepts of the lines of spectral concentration in the Gaussian case are given under appropriate conditions. The methods determine rates of convergence sufficiently fast as the sample ...
• Closed form expressions for Bayesian sample size AI翻译
  作者：Clarke, B.; Yuan, Ao
  作者单位：University of British Columbia; Howard University
  摘要：Sample size criteria are often expressed in terms of the concentration of the posterior density, as controlled by some sort of error bound. Since this is done pre-experimentally, one can regard the posterior density as a function of the data. Thus, when a sample size criterion is formalized in terms of a functional of the posterior, its value is a random variable. Generally, such functionals have means under the true distribution. We give asymptotic expressions for the expected value, under a ...
• Assessing extrema of empirical principal component functions AI翻译
  作者：Hall, Peter; Vial, Celine
  作者单位：Australian National University; Universite Paris Nanterre
  摘要：The difficulties of estimating and representing the distributions of functional data mean that principal component methods play a substantially greater role in functional data analysis than in more conventional finite-dimensional settings. Local maxima and minima in principal component functions are of direct importance; they indicate places in the domain of a random function where influence on the function value tends to be relatively strong but of opposite sign. We explore statistical proper...
• Pseudo-maximum likelihood estimation of ARCH(∞) models AI翻译
  作者：Robinson, Peter M.; Zaffaroni, Paolo
  作者单位：University of London; London School Economics & Political Science; Imperial College London
  摘要：Strong consistency and asymptotic normality of the Gaussian pseudomaximum likelihood estimate of the parameters in a wide class of ARCH(infinity) processes are established. The conditions are shown to hold in case of exponential and hyperbolic decay in the ARCH weights, though in the latter case a faster decay rate is required for the central limit theorem than for the law of large numbers. Particular parameterizations are discussed.
• Generalized score test of homogeneity for mixed effects models AI翻译
  作者：Zhu, Hongtu; Zhang, Heping
  作者单位：Columbia University; New York State Psychiatry Institute; Yale University; Jiangxi Normal University
  摘要：Many important problems in psychology and biomedical studies require testing for overdispersion, correlation and heterogeneity in mixed effects and latent variable models, and score tests are particularly useful for this purpose. But the existing testing procedures depend on restrictive assumptions. In this paper we propose a class of test statistics based on a general mixed effects model to test the homogeneity hypothesis that all of the variance components are zero. Under some mild condition...
• High-dimensional graphs and variable selection with the Lasso AI翻译
  作者：Meinshausen, Nicolai; Buehlmann, Peter
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for...
• Semiparametric estimation of a two-component mixture model AI翻译
  作者：Bordes, Laurent; Mottelet, Stephane; Vandekerkhove, Pierre
  作者单位：Universite de Technologie de Compiegne; Universite Gustave-Eiffel
  摘要：Suppose that univariate data are drawn from a mixture of two distributions that are equal up to a shift parameter. Such a model is known to be nonidentifiable from a nonparametric viewpoint. However, if we assume that the unknown mixed distribution is symmetric, we obtain the identifiability of this model, which is then defined by four unknown parameters: the mixing proportion, two location parameters and the cumulative distribution function of the symmetric mixed distribution. We propose esti...
• Estimation in semiparametric spatial regression AI翻译
  作者：Gao, Jiti; Lu, Zudi; Tjostheim, Dag
  作者单位：University of Western Australia; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of London; London School Economics & Political Science; University of Bergen
  摘要：Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given its closest neighbors requires a four-dimensional nonparametric regression. In this paper a serniparametric spatial regression approach is proposed to avoid this problem. An estimation procedure based o...