Journal of the Royal Statistical Society: Series B

最新文章

• Dynamic models for spatiotemporal data AI翻译
  作者：Stroud, JR; Müller, P; Sansó, B
  作者单位：University of Chicago; Duke University; Simon Bolivar University
  摘要：We propose a model for non-stationary spatiotemporal data. To account for spatial variability, we model the mean function at each time period as a locally weighted mixture of linear regressions. To incorporate temporal variation, we allow the regression coefficients to change through time, The model is cast In a Gaussian state space framework, which allows us to include temporal components such as trends, seasonal effects and autoregressions, and permits a fast implementation and full probabil...
• Non-conjugate prior distribution assessment for multivariate normal sampling AI翻译
  作者：Garthwaite, PH; Al-Awadhi, SA
  作者单位：Open University - UK; Kuwait University
  摘要：Elicitation methods are proposed for quantifying expert opinion about a multivariate normal sampling model. The natural conjugate prior family imposes a relationship between the mean vector and the covariance matrix that can portray an expert's opinion poorly. Instead we assume that opinions about the mean and the covariance are independent and suggest innovative forms of question which enable the expert to quantify separately his or her opinion about each of these parameters. Prior opinion ab...
• Detection of interactions in experiments on large numbers of factors AI翻译
  作者：Lewis, SM; Dean, AM
  作者单位：University of Southampton; University System of Ohio; Ohio State University
  摘要：One of the main advantages of factorial experiments is the information that they can offer on interactions. When there are many factors to be studied, some or all of this information is often sacrificed to keep the size of an experiment economically feasible. Two strategies for group screening are presented for a large number of factors, over two stages of experimentation, with particular emphasis on the detection of interactions. One approach estimates only main effects at the first stage (cl...
• Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics AI翻译
  作者：Barndorff-Nielsen, OE; Shephard, N
  作者单位：University of Oxford; Aarhus University
  摘要：Non-Gaussian processes of Ornstein-Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic ...
• Detection of interactions in experiments on large numbers of factors - Discussion AI翻译
  作者：Bogacka, B; Wynn, H; Cox, DR; Garratt, M; Bailey, RA; Gilmour, SG; Curnow, RN; Longford, NT; Laycock, PJ; Atkinson, A; Torsney, B; Mee, R; Ankenman, BE; Disney, J; Mukerjee, R; Pan, GH; Wu, CFJ
  作者单位：University of London
• Estimating the structural dimension of regressions via parametric inverse regression AI翻译
  作者：Bura, E; Cook, RD
  作者单位：George Washington University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：A new estimation method for the dimension of a regression at the outset of an analysis is proposed. A linear subspace spanned by projections of the regressor vector X, which contains part or all of the modelling information for the regression of a vector Y on X, and its dimension are estimated via the means of parametric inverse regression. Smooth parametric curves are fitted to the p inverse regressions via a multivariate linear model. No restrictions are placed on the distribution of the reg...
• 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...