2005

最新文章

• Modelling directional dispersion through hyperspherical log-splines AI翻译
  作者：Ferreira, JTAS; Steel, MFJ
  作者单位：University of Warwick
  摘要：We introduce the directionally dispersed class of multivariate distributions, a generalization of the elliptical class. By allowing dispersion of multivariate random variables to vary with direction it is possible to generate a very wide and flexible class of distributions. Directionally dispersed distributions have a simple form for their density, which extends a spherically symmetric density function by including a function D modelling directional dispersion. Under a mild condition, the clas...
• Sparsity and smoothness via the fused lasso AI翻译
  作者：Tibshirani, R; Saunders, M; Rosset, S; Zhu, J; Knight, K
  作者单位：Stanford University; International Business Machines (IBM); IBM USA; University of Michigan System; University of Michigan; University of Toronto
  摘要：The lasso penalizes a least squares regression by the sum of the absolute values (L-1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to 0). We propose the 'fused lasso', a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso penalizes the L-1-norm of both the coefficients and their successive differences. Thus it encourages sparsity of the coefficients and also spars...
• Statistical inference for discretely observed Markov jump processes AI翻译
  作者：Bladt, M; Sorensen, M
  作者单位：University of Copenhagen; Universidad Nacional Autonoma de Mexico
  摘要：Likelihood inference for discretely observed Markov jump processes with finite state space is investigated. The existence and uniqueness of the maximum likelihood estimator of the intensity matrix are investigated. This topic is closely related to the imbedding problem for Markov chains. It is demonstrated that the maximum likelihood estimator can be found either by the EM algorithm or by a Markov chain Monte Carlo procedure. When the maximum likelihood estimator does not exist, an estimator c...
• Statistical methods for regular monitoring data AI翻译
  作者：Stein, ML
  作者单位：University of Chicago
  摘要：Meteorological and environmental data that are collected at regular time intervals on a fixed monitoring network can be usefully studied combining ideas from multiple time series and spatial statistics, particularly when there are little or no missing data. This work investigates methods for modelling such data and ways of approximating the associated likelihood functions. Models for processes on the sphere crossed with time are emphasized, especially models that are not fully symmetric in spa...
• Good randomized sequential probability forecasting is always possible AI翻译
  作者：Vovk, V; Shafer, G
  作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark; University of London; Royal Holloway University London
  摘要：Building on the game theoretic framework for probability, we show that it is possible, using randomization, to make sequential probability forecasts that will pass any given battery of statistical tests. This result, an easy consequence of von Neumann's minimax theorem, simplifies and generalizes work by earlier researchers.
• Estimated estimating equations: semiparametric inference for clustered and longitudinal data AI翻译
  作者：Chiou, JM; Müller, HG
  作者单位：University of California System; University of California Davis; Academia Sinica - Taiwan
  摘要：We introduce a flexible marginal modelling approach for statistical inference for clustered and longitudinal data under minimal assumptions. This estimated estimating equations approach is semiparametric and the proposed models are fitted by quasi-likelihood regression, where the unknown marginal means are a function of the fixed effects linear predictor with unknown smooth link, and variance-covariance is an unknown smooth function of the marginal means. We propose to estimate the nonparametr...
• Residual analysis for spatial point processes - Discussion AI翻译
  作者：Kendall, WS; Renshaw, E; Lawson, A; Mateu, J; Saura, F; Harding, EF; Stoyan, D; Zhuang, JC; Besag, J; Diggle, P; Fearnhead, P; Geyer, CJ; Grabarnik, P; Ogata, Y; Schoenberg, FP; Waagepetersen, R
  作者单位：University of Warwick; University of Strathclyde; University of South Carolina System; University of South Carolina Columbia; Universitat Jaume I; Technical University Freiberg; Chalmers University of Technology; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of Washington; University of Washington Seattle; Lancaster University; University of Minnesota System; University of Minnesota Twin Cities; Russian Academy of Sciences; University of California System; University of California Los Angeles; Aalborg University
• An invitation to quantum tomography AI翻译
  作者：Artiles, LM; Gill, RD; Guta, MI
  作者单位：Utrecht University
  摘要：We describe quantum tomography as an inverse statistical problem in which the quantum state of a light beam is the unknown parameter and the data are given by results of measurements performed on identical quantum systems. The state can be represented as an infinite dimensional density matrix or equivalently as a density on the plane called the Wigner function. We present consistency results for pattern function projection estimators and for sieve maximum likelihood estimators for both the den...
• Ascent-based Monte Carlo expectation-maximization AI翻译
  作者：Caffo, BS; Jank, W; Jones, GL
  作者单位：Johns Hopkins University; University System of Maryland; University of Maryland College Park; University of Minnesota System; University of Minnesota Twin Cities
  摘要：The expectation-maximization (EM) algorithm is a popular tool for maximizing likelihood functions in the presence of missing data. Unfortunately, EM often requires the evaluation of analytically intractable and high dimensional integrals. The Monte Carlo EM (MCEM) algorithm is the natural extension of EM that employs Monte Carlo methods to estimate the relevant integrals. Typically, a very large Monte Carlo sample size is required to estimate these integrals within an acceptable tolerance when...
• Self-weighted least absolute deviation estimation for infinite variance autoregressive models AI翻译
  作者：Ling, SQ
  作者单位：Hong Kong University of Science & Technology
  摘要：How to undertake statistical inference for infinite variance autoregressive models has been a long-standing open problem. To solve this problem, we propose a self-weighted least absolute deviation estimator and show that this estimator is asymptotically normal if the density of errors and its derivative are uniformly bounded. Furthermore, a Wald test statistic is developed for the linear restriction on the parameters, and it is shown to have non-trivial local power. Simulation experiments are ...