2022

最新文章

• Selective inference for effect modification via the lasso AI翻译
  作者：Zhao, Qingyuan; Small, Dylan S.; Ertefaie, Ashkan
  作者单位：University of Cambridge; University of Pennsylvania; University of Rochester
  摘要：Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters...
• An approximation algorithm for blocking of an experimental design AI翻译
  作者：Karmakar, Bikram
  作者单位：State University System of Florida; University of Florida
  摘要：Blocked randomized designs are used to improve the precision of treatment effect estimates compared to a completely randomized design. A block is a set of units that are relatively homogeneous and consequently would tend to produce relatively similar outcomes if the treatment had no effect. The problem of finding the optimal blocking of the units into equal sized blocks of any given size larger than two is known to be a difficult problem-there is no polynomial time method guaranteed to find th...
• 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...