3期

最新文章

• Computer model calibration with confidence and consistency AI翻译
  作者：Plumlee, Matthew
  作者单位：Northwestern University
  摘要：The paper proposes and examines a calibration method for inexact models. The method produces a confidence set on the parameters that includes the best parameter with a desired probability under any sample size. Additionally, this confidence set is shown to be consistent in that it excludes suboptimal parameters in large sample environments. The method works and the results hold with few assumptions; the ideas are maintained even with discrete input spaces or parameter spaces. Computation of th...
• Scalable importance tempering and Bayesian variable selection AI翻译
  作者：Zanella, Giacomo; Roberts, Gareth
  作者单位：Bocconi University; University of Warwick
  摘要：We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo methods and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to outperform standard scheme...
• Robust causal structure learning with some hidden variables AI翻译
  作者：Frot, Benjamin; Nandy, Preetam; Maathuis, Marloes H.
  作者单位：University of Pennsylvania
  摘要：We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among the observed variables is sparse, and there are a few hidden variables that have a direct effect on many of the observed variables. Building on the so-called low rank plus sparse framework, we suggest a two-stage approach which first removes the effect of the hidden variables and then estimates the Markov equivalenc...
• Narrowest-over-threshold detection of multiple change points and change-point-like features AI翻译
  作者：Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr
  作者单位：University of London; London School Economics & Political Science
  摘要：We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change points in the piecewise constant signal model, kinks in the piecewise linear signal model and other similar irregularities, which we also ref...
• Intrinsic Gaussian processes on complex constrained domains AI翻译
  作者：Niu, Mu; Cheung, Pokman; Lin, Lizhen; Dai, Zhenwen; Lawrence, Neil; Dunson, David
  作者单位：University of Plymouth; University of Notre Dame; University of Sheffield; Amazon.com; Duke University
  摘要：We propose a class of intrinsic Gaussian processes (GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregularly shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, intrinsic GPs can accommodate spatial domains arising as complex subsets of Euclidean space. Intrinsic GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces. The k...
• Lack-of-fit tests for quantile regression models AI翻译
  作者：Dong, Chen; Li, Guodong; Feng, Xingdong
  作者单位：Shanghai University of Finance & Economics; University of Hong Kong
  摘要：The paper novelly transforms lack-of-fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two-sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test can maintain the power whe...
• A general asymptotic framework for distribution-free graph-based two-sample tests AI翻译
  作者：Bhattacharya, Bhaswar B.
  作者单位：University of Pennsylvania
  摘要：Testing equality of two multivariate distributions is a classical problem for which many non-parametric tests have been proposed over the years. Most of the popular two-sample tests, which are asymptotically distribution free, are based either on geometric graphs constructed by using interpoint distances between the observations (multivariate generalizations of the Wald-Wolfowitz runs test) or on multivariate data depth (generalizations of the Mann-Whitney rank test). The paper introduces a ge...
• Post-selection estimation and testing following aggregate association tests AI翻译
  作者：Heller, Ruth; Meir, Amit; Chatterjee, Nilanjan
  作者单位：Tel Aviv University; University of Washington; University of Washington Seattle; Johns Hopkins University
  摘要：The practice of pooling several individual test statistics to form aggregate tests is common in many statistical applications where individual tests may be underpowered. Although selection by aggregate tests can serve to increase power, the selection process invalidates inference based on the individual test statistics, making it difficult to identify those that drive the signal in follow-up inference. Here, we develop a general approach for valid inference following selection by aggregate tes...