2023

最新文章

• Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC AI翻译
  作者：Sell, Torben; Singh, Sumeetpal Sidhu
  作者单位：University of Edinburgh; University of Cambridge
  摘要：This paper introduces a new neural network based prior for real valued functions. Each weight and bias of the neural network has an independent Gaussian prior, with the key novelty that the variances decrease in the width of the network in such a way that the resulting function is well defined in the limit of an infinite width network. We show that the induced posterior over functions is amenable to Monte Carlo sampling using Hilbert space Markov chain Monte Carlo (MCMC) methods. This type of ...
• Yunxiao Chen and Gongjun Xu's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Chen, Yunxiao; Xu, Gongjun
  作者单位：University of London; London School Economics & Political Science; University of Michigan System; University of Michigan; University of London; London School Economics & Political Science
• Robust estimation and inference for expected shortfall regression with many regressors AI翻译
  作者：He, Xuming; Tan, Kean Ming; Zhou, Wen-Xin
  作者单位：Washington University (WUSTL); University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：Expected shortfall (ES), also known as superquantile or conditional value-at-risk, is an important measure in risk analysis and stochastic optimisation and has applications beyond these fields. In finance, it refers to the conditional expected return of an asset given that the return is below some quantile of its distribution. In this paper, we consider a joint regression framework recently proposed to model the quantile and ES of a response variable simultaneously, given a set of covariates. ...
• Ordering factorial experiments (Apr, qkad027, 2023) AI翻译
  作者：Yang, Liuqing; Zhou, Yongdao; Liu, Min-Qian
• Permutation-based true discovery guarantee by sum tests AI翻译
  作者：Vesely, Anna; Finos, Livio; Goeman, Jelle J.
  作者单位：University of Bremen; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Padua; Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
  摘要：Sum-based global tests are highly popular in multiple hypothesis testing. In this paper, we propose a general closed testing procedure for sum tests, which provides lower confidence bounds for the proportion of true discoveries (TDPs), simultaneously over all subsets of hypotheses. These simultaneous inferences come for free, i.e., without any adjustment of the alpha-level, whenever a global test is used. Our method allows for an exploratory approach, as simultaneity ensures control of the TDP...
• Statistical inference for high-dimensional panel functional time series AI翻译
  作者：Zhou, Zhou; Dette, Holger
  作者单位：University of Toronto; Ruhr University Bochum; University of Toronto
  摘要：In this paper, we develop statistical inference tools for high-dimensional functional time series. We introduce a new concept of physical dependent processes in the space of square integrable functions, which adopts the idea of basis decomposition of functional data in these spaces, and derive Gaussian and multiplier bootstrap approximations for sums of high-dimensional functional time series. These results have numerous important statistical consequences. Exemplarily, we consider the developm...
• A quantitative Heppes theorem and multivariate Bernoulli distributions AI翻译
  作者：Fraiman, Ricardo; Moreno, Leonardo; Ransford, Thomas
  作者单位：Universidad de la Republica, Uruguay; Universidad de la Republica, Uruguay; Laval University
  摘要：Using some extensions of a theorem of Heppes on finitely supported discrete probability measures, we address the problems of classification and testing based on projections. In particular, when the support of the distributions is known in advance (as for instance for multivariate Bernoulli distributions), a single suitably chosen projection determines the distribution. Several applications of these results are considered.
• Bertrand Clarke's contribution to the Discussion of Martingale Posterior Distributions by Fong, Holmes and Walker AI翻译
  作者：Clarke, Bertrand
  作者单位：University of Nebraska System; University of Nebraska Lincoln
• Junhui Cai, Dan Yang, Linda Zhao and Wu Zhu's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Cai, Junhui; Yang, Dan; Zhao, Linda; Zhu, Wu
  作者单位：University of Pennsylvania; University of Hong Kong; Tsinghua University; University of Notre Dame
• Estimating heterogeneous treatment effects with right-censored data via causal survival forests AI翻译
  作者：Cui, Yifan; Kosorok, Michael R.; Sverdrup, Erik; Wager, Stefan; Zhu, Ruoqing
  作者单位：Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Stanford University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perf...