Journal of the Royal Statistical Society: Series B

最新文章

• General Bayesian loss function selection and the use of improper models AI翻译
  作者：Jewson, Jack; Rossell, David
  作者单位：Pompeu Fabra University; Barcelona School of Economics
  摘要：Statisticians often face the choice between using probability models or a paradigm defined by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into a proper probability model, there are many tools to decide which model or loss is more appropriate for the observed data, in the sense of explaining the data's nature. However, when the loss leads to an improper model, there are no principled ways to guide this choice. We address this task by combining the Hyva...
• Universal prediction band via semi-definite programming AI翻译
  作者：Liang, Tengyuan
  作者单位：University of Chicago
  摘要：We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverag...
• Ilya Shpitser's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Shpitser, Ilya
  作者单位：Johns Hopkins University
• Authors' reply to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Vansteelandt, Stijn; Dukes, Oliver
  作者单位：Ghent University
• On functional processes with multiple discontinuities AI翻译
  作者：Li, Jialiang; Li, Yaguang; Hsing, Tailen
  作者单位：National University of Singapore; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Michigan System; University of Michigan
  摘要：We consider the problem of estimating multiple change points for a functional data process. There are numerous examples in science and finance in which the process of interest may be subject to some sudden changes in the mean. The process data that are not in a close vicinity of any change point can be analysed by the usual nonparametric smoothing methods. However, the data close to change points and contain the most pertinent information of structural breaks need to be handled with special ca...
• Linear regression and its inference on noisy network-linked data AI翻译
  作者：Le, Can M.; Li, Tianxi
  作者单位：University of California System; University of California Davis; University of Virginia
  摘要：Linear regression on network-linked observations has been an essential tool in modelling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors. This paper proposes a regression model with non-parametric network effects. The model does not assume that the relational data or network structure is exactly obser...
• Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models AI翻译
  作者：Graham, Matthew M.; Thiery, Alexandre H.; Beskos, Alexandros
  作者单位：University of London; University College London; National University of Singapore
  摘要：Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology-borrowing ideas from statistical physics and computational chemistry-for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process. Joint configurations of the underlying...
• Optimal thinning of MCMC output AI翻译
  作者：Riabiz, Marina; Chen, Wilson Ye; Cockayne, Jon; Swietach, Pawel; Niederer, Steven A.; Mackey, Lester; Oates, Chris J.
  作者单位：University of London; King's College London; Alan Turing Institute; University of Sydney; University of Oxford; Microsoft; Newcastle University - UK
  摘要：The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Typically a number of the initial states are attributed to 'burn in' and removed, while the remainder of the chain is 'thinned' if compression is also required. In this paper, we consider the problem of retrospectively selecting a subset of states, of fixed cardinality, from the sample path such that the approximation...
• Graphical criteria for efficient total effect estimation via adjustment in causal linear models AI翻译
  作者：Henckel, Leonard; Perkovic, Emilija; Maathuis, Marloes H.
  作者单位：University of Copenhagen; University of Washington; University of Washington Seattle; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total causal effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce a graphical criterion to compare the asymptotic variances provided by certain valid adjustment...
• Functional structural equation model AI翻译
  作者：Lee, Kuang-Yao; Li, Lexin
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of California System; University of California Berkeley
  摘要：In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental functions is nonlinear. We then d...