Journal of the Royal Statistical Society: Series B

最新文章

• Multiscale adaptive regression models for neuroimaging data AI翻译
  作者：Li, Yimei; Zhu, Hongtu; Shen, Dinggang; Lin, Weili; Gilmore, John H.; Ibrahim, Joseph G.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：Neuroimaging studies aim to analyse imaging data with complex spatial patterns in a large number of locations (called voxels) on a two-dimensional surface or in a three-dimensional volume. Conventional analyses of imaging data include two sequential steps: spatially smoothing imaging data and then independently fitting a statistical model at each voxel. However, conventional analyses suffer from the same amount of smoothing throughout the whole image, the arbitrary choice of extent of smoothin...
• Robustness and accuracy of methods for high dimensional data analysis based on Student's t-statistic AI翻译
  作者：Delaigle, Aurore; Hall, Peter; Jin, Jiashun
  作者单位：University of Melbourne; University of California System; University of California Davis; Carnegie Mellon University
  摘要：Student's t-statistic is finding applications today that were never envisaged when it was introduced more than a century ago. Many of these applications rely on properties, e.g. robustness against heavy-tailed sampling distributions, that were not explicitly considered until relatively recently. We explore these features of the t-statistic in the context of its application to very high dimensional problems, including feature selection and ranking, the simultaneous testing of many different hyp...
• Self-consistent method for density estimation AI翻译
  作者：Bernacchia, Alberto; Pigolotti, Simone
  作者单位：Yale University; University of Copenhagen; Niels Bohr Institute
  摘要：The estimation of a density profile from experimental data points is a challenging problem, which is usually tackled by plotting a histogram. Prior assumptions on the nature of the density, from its smoothness to the specification of its form, allow the design of more accurate estimation procedures, such as maximum likelihood. Our aim is to construct a procedure that makes no explicit assumptions, but still providing an accurate estimate of the density. We introduce the self-consistent estimat...
• Riemann manifold Langevin and Hamiltonian Monte Carlo methods AI翻译
  作者：Girolami, Mark; Calderhead, Ben
  作者单位：University of London; University College London
  摘要：The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis-Hastings or indeed Hamiltonian Monte Carlo and Metropoli...
• Adaptive inference for the mean of a Gaussian process in functional data AI翻译
  作者：Bunea, Florentina; Ivanescu, Andrada E.; Wegkamp, Marten H.
  作者单位：State University System of Florida; Florida State University; University of North Carolina; East Carolina University
  摘要：We propose and analyse fully data-driven methods for inference about the mean function of a Gaussian process from a sample of independent trajectories of the process, observed at random time points and corrupted by additive random error. Our methods are based on thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are determined from the data and the resulting estimates adapt to the unknown sparsity of the mean function relative to the...
• A geometric characterization of optimal designs for regression models with correlated observations AI翻译
  作者：Holland-Letz, Tim; Dette, Holger; Pepelyshev, Andrey
  作者单位：Ruhr University Bochum; University of Sheffield
  摘要：We consider the problem of optimal design of experiments for random-effects models, especially population models, where a small number of correlated observations can be taken on each individual, whereas the observations corresponding to different individuals are assumed to be uncorrelated. We focus on c-optimal design problems and show that the classical equivalence theorem and the famous geometric characterization of Elfving from the case of uncorrelated data can be adapted to the problem of ...
• Bayesian smoothing of photon-limited images with applications in astronomy AI翻译
  作者：White, John Thomas; Ghosal, Subhashis
  作者单位：North Carolina State University
  摘要：We consider a multiscale model for intensities in photon-limited images using a Bayesian framework. A typical Dirichlet prior on relative intensities is not efficient in picking up structures owing to the continuity of intensities. We propose a novel prior using the so-called 'Chinese restaurant process' to create structures in the form of equal intensities of some neighbouring pixels. Simulations are conducted using several photon-limited images, which are common in X-ray astronomy and other ...
• Asymptotic behaviour of the posterior distribution in overfitted mixture models AI翻译
  作者：Rousseau, Judith; Mengersen, Kerrie
  作者单位：Universite PSL; Universite Paris-Dauphine; Institut Polytechnique de Paris; ENSAE Paris; Queensland University of Technology (QUT)
  摘要：We study the asymptotic behaviour of the posterior distribution in a mixture model when the number of components in the mixture is larger than the true number of components: a situation which is commonly referred to as an overfitted mixture. We prove in particular that quite generally the posterior distribution has a stable and interesting behaviour, since it tends to empty the extra components. This stability is achieved under some restriction on the prior, which can be used as a guideline fo...
• Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models AI翻译
  作者：Wood, Simon N.
  作者单位：University of Bath
  摘要：Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing parameter selection in semiparametric regression. However, existing REML or marginal likelihood (ML) based methods for semiparametric generalized linear models (GLMs) use iterative REML or ML estimation of the smoothing parameters of working linear approximations to the GLM. Such indirect schemes need not converge and fail...
• Augmented designs to assess principal strata direct effects AI翻译
  作者：Mattei, Alessandra; Mealli, Fabrizia
  作者单位：University of Florence
  摘要：Many studies involving causal questions are often concerned with understanding the causal pathways by which a treatment affects an outcome. Thus, the concept of 'direct' versus 'indirect' effects comes into play. We tackle the problem of disentangling direct and indirect effects by investigating new augmented experimental designs, where the treatment is randomized, and the mediating variable is not forced, but only randomly encouraged. There are two key features of our framework: we adopt a pr...