Journal of the Royal Statistical Society: Series B

最新文章

• Qing Yang and Xin Tong's contribution to the Discussion of 'Root and community inference on the latent growth process of a network' by Crane and Xu AI翻译
  作者：Yang, Qing; Tong, Xin
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California
• Automatic change-point detection in time series via deep learning AI翻译
  作者：Li, Jie; Fearnhead, Paul; Fryzlewicz, Piotr; Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science; Lancaster University
  摘要：Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated b...
• A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference AI翻译
  作者：Atchade, Yves; Wang, Liwei
  作者单位：Boston University; Boston University
  摘要：We propose a very fast approximate Markov chain Monte Carlo sampling framework that is applicable to a large class of sparse Bayesian inference problems. The computational cost per iteration in several regression models is of order O(n(s+J)), where n is the sample size, s is the underlying sparsity of the model, and J is the size of a randomly selected subset of regressors. This cost can be further reduced by data sub-sampling when stochastic gradient Langevin dynamics are employed. The algori...
• On the instrumental variable estimation with many weak and invalid instruments AI翻译
  作者：Lin, Yiqi; Windmeijer, Frank; Song, Xinyuan; Fan, Qingliang
  作者单位：Chinese University of Hong Kong; University of Oxford; University of Oxford; Chinese University of Hong Kong; Chinese University of Hong Kong
  摘要：We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the 'sparsest rule', which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs. Furthermore, we propose a surro...
• Authors' reply to the Discussion of 'Estimating means of bounded random variables by betting' AI翻译
  作者：Waudby-Smith, Ian; Ramdas, Aaditya
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
• GRASP: a goodness-of-fit test for classification learning AI翻译
  作者：Javanmard, Adel; Mehrabi, Mohammad
  作者单位：University of Southern California; University of Southern California
  摘要：Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterising the fit of the model to the underlying conditional law of labels given the features vector (Y | X), e.g. due to model misspecification, over fitting, and high-dimensionality. In this paper, we consider the fundamental problem of assessing the goodness-of-fit for a general binary classifier. Our framework does not make any parametric ...
• Spatial confidence regions for combinations of excursion sets in image analysis AI翻译
  作者：Maullin-Sapey, Thomas; Schwartzman, Armin; Nichols, Thomas E.
  作者单位：University of Oxford; University of California System; University of California San Diego; University of California System; University of California San Diego; University of Oxford
  摘要：The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide ...
• Continuously indexed graphical models AI翻译
  作者：Waghmare, Kartik G.; Panaretos, Victor M.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Let X={Xu}u is an element of U be a real-valued Gaussian process indexed by a set U. We show that X can be viewed as a graphical model with an uncountably infinite graph, where each Xu is a vertex. This graph is characterized by the reproducing property of X's covariance kernel, without restricting U to be finite or countable, allowing the modelling of stochastic processes in continuous time/space. Unlike traditional methods, this characterization is not based on zero entries of an inverse cov...
• A nonparametric framework for treatment effect modifier discovery in high dimensions AI翻译
  作者：Boileau, Philippe; Leng, Ning; Hejazi, Nima S.; van der Laan, Mark; Dudoit, Sandrine
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Roche Holding; Genentech; Roche Holding USA; Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：Heterogeneous treatment effects are driven by treatment effect modifiers (TEMs), pretreatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering these variables are limited to low-dimensional data, data with weakly correlated covariates, or data generated according to parametric processes. We resolve these issues by proposing a framework for defining model-agnostic TEM variable importance parameters (TEM-VIPs), deriving one-step, estimating equati...
• Neural networks meet random forests AI翻译
  作者：Qiu, Rui; Xu, Shuntuo; Yu, Zhou
  作者单位：East China Normal University
  摘要：Neural networks and random forests are popular and promising tools for machine learning. This article explores the proper integration of these two approaches for nonparametric regression to improve the performance of a single approach. Specifically, we propose a neural network estimator with local enhancement provided by random forests. It naturally synthesizes the local relation adaptivity of random forests and the strong global approximation ability of neural networks. Based on the classical...