3期

最新文章

• Niwen Zhou and Xu Guo's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Zhou, Niwen; Guo, Xu
  作者单位：Beijing Normal University; Beijing Normal University
• Jiwei Zhao's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Zhao, Jiwei
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• Robust generalised Bayesian inference for intractable likelihoods AI翻译
  作者：Matsubara, Takuo; Knoblauch, Jeremias; Briol, Francois-Xavier; Oates, Chris J.
  作者单位：Newcastle University - UK; Alan Turing Institute; University of London; University College London
  摘要：Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood. Here we consider generalised Bayesian inference with a Stein discrepancy as a loss function, motivated by applications in which the likelihood contains an intractable normalisation constant. In this context, the Stein discrepancy circumvents evaluation of the normalisation constant and produces...
• Peng Ding's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Ding, Peng
  作者单位：University of California System; University of California Berkeley
• Chaohua Dong, Jiti Gao and Oliver Linton's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Dong, Chaohua; Gao, Jiti; Linton, Oliver
  作者单位：Zhongnan University of Economics & Law; Monash University; University of Cambridge
• 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
• 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 ...
• 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
• 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
• 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...