2001

最新文章

• Bayesian calibration of computer models - Discussion AI翻译
  作者：Wynn, HP; Brown, PJ; Anderson, C; Rougier, JC; Diggle, PJ; Goldstein, M; Kendall, WS; Craig, P; Beven, K; Campbell, K; McKay, MD; Challenor, P; Cooke, RM; Higgins, NA; Jones, JA; Kleijnen, JPC; Notz, W; Santner, T; Williams, B; Lehman, J; Saltelli, A; Shephard, N; Tjelmeland, H; Kennedy, MC; O'Hagan, A
  作者单位：University of Warwick; University of Kent; University of Sheffield; Durham University; Lancaster University; United States Department of Energy (DOE); Los Alamos National Laboratory; NERC National Oceanography Centre; University of Southampton; Delft University of Technology; Tilburg University; University System of Ohio; Ohio State University; European Commission Joint Research Centre; University of Oxford; Norwegian University of Science & Technology (NTNU)
• Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation AI翻译
  作者：Gu, MG; Zhu, HT
  作者单位：Chinese University of Hong Kong; University of Victoria
  摘要：We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis-Hastings-Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion is built into the algorithm so stopping is automatic once the desired precision has been set. Simulation studies and applications to some real data set...
• Inference in high dimensional generalized linear models based on soft thresholding AI翻译
  作者：Klinger, A
  作者单位：University of Munich
  摘要：We further develop and analyse penalized likelihood estimators for generalized linear models with a large number of coefficients. The methodology proposed leads to an adaptive selection of model terms without substantial variance inflation. Our proposal extends the soft thresholding strategy of Donoho and Johnstone and the lasso of Tibshirani to generalized linear models and multiple predictor variables. In addition, we develop an estimator for the covariance matrix of the estimated coefficien...
• Spatiotemporal prediction for log-Gaussian Cox processes AI翻译
  作者：Brix, A; Diggle, PJ
  作者单位：Lancaster University
  摘要：Space-time point pattern data have become more widely available as a result of technological developments In areas such as geographic information systems. We describe a flexible class of space-time point processes. Our models are Cox processes whose stochastic intensity is a space-time Ornstein-Uhlenbeck process. We develop moment-based methods of parameter estimation, show how to predict the underlying intensity by using a Markov chain Monte Carlo approach and illustrate the performance of ou...
• Perfect slice samplers AI翻译
  作者：Mira, A; Moller, J; Roberts, GO
  作者单位：Lancaster University; University of Insubria; Aalborg University
  摘要：Perfect sampling allows the exact simulation of random variables from the stationary measure of a Markov chain. By exploiting monotonicity properties of the slice sampler we show that a perfect version of the algorithm can be easily implemented, at least when the target distribution is bounded. Various extensions, including perfect product slice samplers, and examples of applications are discussed.
• A penalized likelihood approach to image warping - Discussion AI翻译
  作者：Molchanov, I; Jennison, C; Ersboll, BK; Hancock, E; Wilson, R; Horgan, G; Kent, JT; Ashburner, J; Dryden, IL; Petrou, M; Angulo, JM; Berman, M; Coleman, R; Duta, N; Jain, AK; Ghosh, JK; Murthy, CA; Gray, A; Gustafsson, J; Rudemo, M; Hogg, D; Koch, I; Linney, A; Ramsay, JO; Ramsay, TO; Rao, MM; Sebastiani, G; de Souza, K; Sun, CM; Buckley, M; Titterington, DM; Trubuil, A; Worsley, KJ; Yu, KM
  作者单位：University of Glasgow; University of Bath; Technical University of Denmark; University of York - UK; James Hutton Institute; University of Leeds; University of London; University College London; University of Nottingham; University of Surrey; University of Granada; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Imperial College London; Michigan State University; Indian Statistical Institute; Indian Statistical Institute Kolkata; University of Strathclyde; Chalmers University of Technology; University of Newcastle; University of London; University College London; McGill University; University of California System; University of California Riverside; INRAE; Universite Paris Saclay; University of Plymouth
• A penalized likelihood approach to image warping AI翻译
  作者：Glasbey, CA; Mardia, KV
  作者单位：James Hutton Institute; University of Leeds
  摘要：A warping is a function that deforms images by mapping between image domains. The choice of function is formulated statistically as maximum penalized likelihood, where the likelihood measures the similarity between images after warping and the penalty is a measure of distortion of a warping. The paper addresses two issues simultaneously, of how to choose the warping function and how to assess the alignment. A new, Fourier-von Mises image model is identified, with phase differences between Four...
• Moment estimation for statistics from marked point processes AI翻译
  作者：Politis, DN; Sherman, M
  作者单位：Texas A&M University System; Texas A&M University College Station; University of California System; University of California San Diego
  摘要：In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L-2-consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated parameters. A practical data-bas...
• On repeated measures analysis with misspecified covariance structure AI翻译
  作者：Crowder, M
  作者单位：Imperial College London
  摘要：In recent years various sophisticated methods have been developed for the analysis of repeated measures. or longitudinal data. The more traditional approach, based on a normal likelihood function, has been shown to be unsatisfactory, in the sense of yielding asymptotically biased estimates when the covariance structure is misspecified. More recent methodology, based on generalized linear models and quasi-likelihood estimation, has gained widespread acceptance as 'generalized estimating equatio...
• Robust estimation for finite populations based on a working model AI翻译
  作者：Kuk, AYC; Welsh, AH
  作者单位：National University of Singapore; Australian National University
  摘要：A common scenario in finite population inference is that it is possible to find a working superpopulation model which explains the main features of the population but which may not capture all the fine details. In addition, there are often outliers in the population which do not follow the assumed superpopulation model. In situations like these, it is still advantageous to make use of the working model to estimate finite population quantities, provided that we do it in a robust manner. The app...