2023

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...
• Blake Moya's contribution to the Discussion of Martingale Posterior Distributions by Fong, Holmes and Walker AI翻译
  作者：Moya, Blake
  作者单位：University of Texas System; University of Texas Austin
• The variational method of moments AI翻译
  作者：Bennett, Andrew; Kallus, Nathan
  作者单位：Cornell University; Cornell University
  摘要：The conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables, a prominent example being instrumental variable regression. We introduce a very general class of estimators called the variational method of moments (VMM), motivated by a variational minimax reformulation of optimally weighted generalized method of moments for finite sets of moments. VMM controls infinitely for many moments characterized by flexible function classes suc...
• Ordering factorial experiments AI翻译
  作者：Yang, Liuqing; Zhou, Yongdao; Liu, Min-Qian
  作者单位：Nankai University; Nankai University
  摘要：In many practical experiments, both the level combinations of factors and the addition orders will affect the responses. However, virtually no construction methods have been provided for such experimental designs. This paper focuses on such experiments, introduces a new type of design called the ordering factorial design, and proposes the nominal main effect component-position model and interaction-main effect component-position model. To obtain efficient fractional designs, we provide some de...
• Vintage factor analysis with Varimax performs statistical inference AI翻译
  作者：Vanderweele, Tyler J.
  作者单位：Harvard University
• Designing to detect heteroscedasticity in a regression model AI翻译
  作者：Lanteri, Alessandro; Leorato, Samantha; Lopez-Fidalgo, Jesus; Tommasi, Chiara
  作者单位：University of Milan; University of Navarra; University of Milan
  摘要：We consider the problem of designing experiments to detect the presence of a specified heteroscedastity in Gaussian regression models. We study the relationship of the D-s- and KL-criteria with the noncentrality parameter of the asymptotic chi-squared distribution of a likelihood-based test, for local alternatives. We found that, when the heteroscedastity depends on one parameter, the two criteria coincide asymptotically and that the D-1-criterion is proportional to the noncentrality parameter...
• John Kent's contribution to the Discussion of the 'Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Li, Jie; Fearnhead, Paul; Fryzlewicz, Piotr; Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science; Lancaster University
• Kanti Mardia's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Mardia, Kanti V.
  作者单位：University of Leeds
• Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap (vol 81, pg 735, 2019) AI翻译
  作者：Zhao, Qingyuan; Small, Dylan S.; Bhattacharya, Bhaswar B.
• Proposer of the vote of thanks to Rohe & Zeng and contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' AI翻译
  作者：Hoff, Peter
  作者单位：Duke University; Duke University