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最新文章

• Covariate adjustment in multiarmed, possibly factorial experiments AI翻译
  作者：Zhao, Anqi; Ding, Peng
  作者单位：National University of Singapore; University of California System; University of California Berkeley
  摘要：Randomized experiments are the gold standard for causal inference and enable unbiased estimation of treatment effects. Regression adjustment provides a convenient way to incorporate covariate information for additional efficiency. This article provides a unified account of its utility for improving estimation efficiency in multiarmed experiments. We start with the commonly used additive and fully interacted models for regression adjustment in estimating average treatment effects (ATE), and cla...
• Conformalized survival analysis AI翻译
  作者：Candes, Emmanuel; Lei, Lihua; Ren, Zhimei
  作者单位：Stanford University; Stanford University; Stanford University; University of Chicago
  摘要：In this paper, we develop an inferential method based on conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariate-dependent lower predictive bounds on survival times. In the Type I right-censoring setting, when the censoring times are completely exogenous, the lower predictive bounds have guaranteed coverage in finite samples without any assumptions other than that of operating on independent and identically distributed data points. Under a...
• Modelling matrix time series via a tensor CP-decomposition AI翻译
  作者：Chang, Jinyuan; He, Jing; Yang, Lin; Yao, Qiwei
  作者单位：Zhejiang Gongshang University; Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science
  摘要：We consider to model matrix time series based on a tensor canonical polyadic (CP)-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. To overcome the intricacy of solving a rank-reduced generalized eigenequation, we propose a further refined approach which projects ...
• Bayesian fusion: scalable unification of distributed statistical analyses AI翻译
  作者：Dai, Hongsheng; Pollock, Murray; Roberts, Gareth O.
  作者单位：University of Essex; Newcastle University - UK; Alan Turing Institute; University of Warwick
  摘要：There has been considerable interest in addressing the problem of unifying distributed analyses into a single coherent inference, which arises in big-data settings, when working under privacy constraints, and in Bayesian model choice. Most existing approaches relied upon approximations of the distributed analyses, which have significant shortcomings-the quality of the inference can degrade rapidly with the number of analyses being unified, and can be substantially biased when unifying analyses...
• Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (vol 80, pg 157, 2018) AI翻译
  作者：Goncalves, Flavio B.; Gamerman, Dani
  作者单位：Universidade Federal de Minas Gerais; Universidade Federal do Rio de Janeiro
• Biased-sample empirical likelihood weighting for missing data problems: an alternative to inverse probability weighting AI翻译
  作者：Liu, Yukun; Fan, Yan
  作者单位：East China Normal University; Shanghai University of International Business & Economics
  摘要：Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable if some probabilities are very close to zero. To overcome this problem, at least three remedies have been developed in the literature: stabilizing, thresholding, and trimming. However, the final estimators are still IPW-type estimators, and inevitably inher...
• Informative core identification in complex networks AI翻译
  作者：Miao, Ruizhong; Li, Tianxi
  作者单位：University of Virginia
  摘要：In a complex network, the core component with interesting structures is usually hidden within noninformative connections. The noises and bias introduced by the noninformative component can obscure the salient structure and limit many network modeling procedures' effectiveness. This paper introduces a novel core-periphery model for the noninformative periphery structure of networks without imposing a specific form of the core. We propose spectral algorithms for core identification for general d...
• On inference in high-dimensional regression AI翻译
  作者：Battey, Heather S.; Reid, Nancy
  作者单位：Imperial College London; University of Toronto
  摘要：This paper develops an approach to inference in a linear regression model when the number of potential explanatory variables is larger than the sample size. The approach treats each regression coefficient in turn as the interest parameter, the remaining coefficients being nuisance parameters, and seeks an optimal interest-respecting transformation, inducing sparsity on the relevant blocks of the notional Fisher information matrix. The induced sparsity is exploited through a marginal least-squa...
• 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 ...
• Usable and precise asymptotics for generalized linear mixed model analysis and design (vol 84, pg 55, 2022) AI翻译
  作者：Jiang, J.; Wand, M. P.; Bhaskaran, A.