Journal of the Royal Statistical Society: Series B

最新文章

• Bivariate location-scale models for regression analysis, with applications to lifetime data AI翻译
  作者：He, WQ; Lawless, JF
  作者单位：University of Toronto; Sinai Health System Toronto; Lunenfeld Tanenbaum Research Institute; University of Waterloo
  摘要：The literature on multivariate linear regression includes multivariate normal models, models that are used in survival analysis and a variety of models that are used in other areas such as econometrics. The paper considers the class of location-scale models, which includes a large proportion of the preceding models. It is shown that, for complete data, the maximum likelihood estimators for regression coefficients in a linear location-scale framework are consistent even when the joint distribut...
• Local model uncertainty and incomplete-data bias - Discussion AI翻译
  作者：Diggle, PJ; Little, R; Liao, TF; Longford, NT; Torsney, B; Parker, H; Smith, PWF; Clarke, PS; Dawid, AP; Skinner, C; Sorensen, M; Atkinson, AC; Chatfield, C; Curnow, R; Draper, D; Ferguson, C; Henderson, N; Onabid, M; Parker, H; Pritchard, G; Sharif, M; Zhu, XM; Wit, E; Greenland, S; Gustafson, P; Iwasaki, M; Manski, CF; McDonald, JW; McGrory, C; Barry, S; Fearnside, A; Nguyen, TM; Lo Conte, R; Weir, J; Miller, J; Recchia, A; Wit, E; Purutçuoglu, V; Wit, E
  作者单位：Lancaster University; University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Urbana-Champaign; University of Glasgow; University of Southampton; Imperial College London; University of London; University College London; University of Copenhagen; University of London; London School Economics & Political Science; University of Bath; University of Reading; University of California System; University of California Santa Cruz; University of California System; University of California Los Angeles; University of British Columbia; Seikei University; Northwestern University; University of Pennsylvania
• On nonparametric maximum likelihood estimation with interval censoring and left truncation AI翻译
  作者：Hudgens, MG
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：A graph theoretical approach is employed to describe the support set of the nonparametric maximum likelihood estimator for the cumulative distribution function given interval-censored and left-truncated data. A necessary and sufficient condition for the existence of a nonparametric maximum likelihood estimator is then derived. Two previously analysed data sets are revisited.
• Scaling limits for the transient phase of local Metropolis-Hastings algorithms AI翻译
  作者：Christensen, OF; Roberts, GO; Rosenthal, JS
  作者单位：Lancaster University; Aarhus University; University of Toronto
  摘要：The paper considers high dimensional Metropolis and Langevin algorithms in their initial transient phase. In stationarity, these algorithms are well understood and it is now well known how to scale their proposal distribution variances. For the random-walk Metropolis algorithm, convergence during the transient phase is extremely regular-to the extent that the algo-rithm's sample path actually resembles a deterministic trajectory. In contrast, the Langevin algorithm with variance scaled to be o...
• Bayesian inference for stochastic multitype epidemics in structured populations via random graphs AI翻译
  作者：Demiris, N; O'Neill, PD
  作者单位：University of Nottingham; MRC Biostatistics Unit
  摘要：The paper is concerned with new methodology for statistical inference for final outcome infectious disease data using certain structured population stochastic epidemic models. A major obstacle to inference for such models is that the likelihood is both analytically and numerically intractable. The approach that is taken here is to impute missing information in the form of a random graph that describes the potential infectious contacts between individuals. This level of imputation overcomes var...
• Markov chain Monte Carlo methods for high dimensional inversion in remote sensing AI翻译
  作者：Haario, H; Laine, M; Lehtinen, M; Saksman, E; Tamminen, J
  作者单位：University of Helsinki; University of Oulu; University of Jyvaskyla; Finnish Meteorological Institute
  摘要：We discuss the inversion of the gas profiles (ozone, NO3, NO2, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10-20 km. The new feature is that these data allow the inversion of the ...
• Approximating likelihoods for large spatial data sets AI翻译
  作者：Stein, ML; Chi, ZY; Welty, LJ
  作者单位：University of Chicago
  摘要：Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for n observations require O(n(3)) operations. Since any joint density can be written as a product of conditional densities based on some ordering of the observations, one way to lessen the computations is to condition on only some of the 'past' observations when computing the conditional densities. We s...
• A method for combining inference across related nonparametric Bayesian models AI翻译
  作者：Müller, P; Quintana, F; Rosner, G
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Pontificia Universidad Catolica de Chile; University of Texas System
  摘要：We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In the nonparametric context, modelling is complicated by the fact that the random quantities over which we define the hierarchy are infinite dimensional. We discuss a formal definition of such a hierarchical model. The approach includes a regression at the level of the nonparame...
• Joint response graphs and separation induced by triangular systems AI翻译
  作者：Wermuth, N; Cox, DR
  作者单位：University of Gothenburg; University of Oxford
  摘要：We consider joint probability distributions generated recursively in terms of univariate conditional distributions satisfying conditional independence restrictions. The independences are captured by missing edges in a directed graph. A matrix form of such a graph, called the generating edge matrix, is triangular so the distributions that are generated over such graphs are called triangular systems. We study consequences of triangular systems after grouping or reordering of the variables for an...
• Exact filtering for partially observed continuous time models AI翻译
  作者：Fearnhead, P; Meligkotsidou, L
  作者单位：Lancaster University
  摘要：The forward-backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, wig show how the forward-backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of...