2022

最新文章

• Kuldeep Kumar's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Kumar, Kuldeep
  作者单位：Bond University
• Sebastian Dietz's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Dietz, Sebastian
• Bayesian estimation and comparison of conditional moment models AI翻译
  作者：Chib, Siddhartha; Shin, Minchul; Simoni, Anna
  作者单位：Washington University (WUSTL); Federal Reserve System - USA; Federal Reserve Bank - Philadelphia; Institut Polytechnique de Paris; Ecole Polytechnique; Centre National de la Recherche Scientifique (CNRS); ENSAE Paris
  摘要：We consider the Bayesian analysis of models in which the unknown distribution of the outcomes is specified up to a set of conditional moment restrictions. The non-parametric exponentially tilted empirical likelihood function is constructed to satisfy a sequence of unconditional moments based on an increasing (in sample size) vector of approximating functions (such as tensor splines based on the splines of each conditioning variable). For any given sample size, results are robust to the number ...
• Gaussian prepivoting for finite population causal inference AI翻译
  作者：Cohen, Peter L.; Fogarty, Colin B.
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：In finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, hypotheses which impute the missing potential outcomes. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject-specific treatment effects unspecified. Tests valid for sharp null hypotheses can be anti-conservative should only the weak null hold. We develop a general framework for u...
• Causal inference with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq AI翻译
  作者：Papadogeorgou, Georgia; Imai, Kosuke; Lyall, Jason; Li, Fan
  作者单位：State University System of Florida; University of Florida; Harvard University; Harvard University; Dartmouth College; Duke University; State University System of Florida; University of Florida
  摘要：Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular stochastic treatment assignment strat...
• Ian Hunt's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Hunt, Ian
  作者单位：University of Tasmania
• Graph based Gaussian processes on restricted domains AI翻译
  作者：Dunson, David B.; Wu, Hau-Tieng; Wu, Nan
  作者单位：Duke University; Duke University
  摘要：In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian process (GP) models, proposing a new class of Graph Laplacian based GPs (GL-GPs), which learn a covariance that respects the geometry of the inp...
• High-dimensional principal component analysis with heterogeneous missingness AI翻译
  作者：Zhu, Ziwei; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; University of Michigan System; University of Michigan; University of London; London School Economics & Political Science
  摘要：We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In a simple, homogeneous observation model, we show that an existing observed-proportion weighted (OPW) estimator of the leading principal components can (nearly) attain the minimax optimal rate of convergence, which exhibits an interesting phase transition. However, deeper investigation reveals that, particularly in more realistic settings where the observation probabilities are heterogeneou...
• Dimension-free mixing for high-dimensional Bayesian variable selection AI翻译
  作者：Zhou, Quan; Yang, Jun; Vats, Dootika; Roberts, Gareth O.; Rosenthal, Jeffrey S.
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Oxford; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; University of Warwick; University of Toronto
  摘要：Yang et al. proved that the symmetric random walk Metropolis-Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. We propose a novel Markov chain Monte Carlo (MCMC) sampler using an informed proposal scheme, which we prove achieves a much faster mixing time that is independent of the number of covariates, under the assumptions of Yang et al. To the best of our knowledge, this is the first high-dimensional result which rigorously shows th...
• Eric J Tchetgen Tchetgen's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Tchetgen, Eric J. Tchetgen
  作者单位：University of Pennsylvania