2022

最新文章

• CovNet: Covariance networks for functional data on multidimensional domains AI翻译
  作者：Sarkar, Soham; Panaretos, Victor M.
  作者单位：Indian Statistical Institute; Indian Statistical Institute Delhi; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical challenges, rendering the standard methods effectively inapplicable. To address this problem, we introduce Covariance Networks (CovNet) as a modelling and estimation tool. The CovNet model is universal-it can be used to approximate any covariance up to desired precision. Moreover, the model can be fitted efficiently to ...
• Calibrating the scan statistic: Finite sample performance versus asymptotics AI翻译
  作者：Walther, Guenther; Perry, Andrew
  作者单位：Stanford University
  摘要：We consider the problem of detecting an elevated mean on an interval with unknown location and length in the univariate Gaussian sequence model. Recent results have shown that using scale-dependent critical values for the scan statistic allows to attain asymptotically optimal detection simultaneously for all signal lengths, thereby improving on the traditional scan, but this procedure has been criticised for losing too much power for short signals. We explain this discrepancy by showing that t...
• Assumption-lean inference for generalised linear model parameters AI翻译
  作者：Vansteelandt, Stijn; Dukes, Oliver
  作者单位：Ghent University; University of London; London School of Hygiene & Tropical Medicine
  摘要：Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent some a priori known, ground truth, making standard inferences prone to bias, but also failin...
• Christian Hennig's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Hennig, Christian
  作者单位：University of Bologna
• Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors AI翻译
  作者：Biswas, Niloy; Bhattacharya, Anirban; Jacob, Pierre E.; Johndrow, James E.
  作者单位：Harvard University; Texas A&M University System; Texas A&M University College Station; ESSEC Business School; University of Pennsylvania
  摘要：We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when each iteration is expensive, as is the case when dealing with modern data sets, such as genome-wide association studies with thousands of rows and up to hundreds of thousands of columns. We develop coupling techniques tailored to the setting of high-d...
• Exact clustering in tensor block model: Statistical optimality and computational limit AI翻译
  作者：Han, Rungang; Luo, Yuetian; Wang, Miaoyan; Zhang, Anru R.
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Duke University; Duke University; Duke University; Duke University
  摘要：High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally efficient methods, high-order Lloyd algorithm (HLloyd), and high-order spectral clustering (HSC), for high-order clustering. The convergence gu...
• SIMPLE: Statistical inference on membership profiles in large networks AI翻译
  作者：Fan, Jianqing; Fan, Yingying; Han, Xiao; Lv, Jinchi
  作者单位：Princeton University; University of Southern California; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：Network data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting of degree-corrected mixed me...
• Gaussian differential privacy AI翻译
  作者：Dong, Jinshuo; Roth, Aaron; Su, Weijie J.
  作者单位：University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analysing important primitives like privacy amplification by suhsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation of differential privacy, which w...
• Jorge Mateu's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Mateu, Jorge
  作者单位：Universitat Jaume I
• False discovery rate control with e-values AI翻译
  作者：Wang, Ruodu; Ramdas, Aaditya
  作者单位：University of Waterloo; Carnegie Mellon University; Carnegie Mellon University
  摘要：E-values have gained attention as potential alternatives to p-values as measures of uncertainty, significance and evidence. In brief, e-values are realized by random variables with expectation at most one under the null; examples include betting scores, (point null) Bayes factors, likelihood ratios and stopped supermartingales. We design a natural analogue of the Benjamini-Hochberg (BH) procedure for false discovery rate (FDR) control that utilizes e-values, called the e-BH procedure, and comp...