2期

最新文章

• Statistical inference for high-dimensional panel functional time series AI翻译
  作者：Zhou, Zhou; Dette, Holger
  作者单位：University of Toronto; Ruhr University Bochum; University of Toronto
  摘要：In this paper, we develop statistical inference tools for high-dimensional functional time series. We introduce a new concept of physical dependent processes in the space of square integrable functions, which adopts the idea of basis decomposition of functional data in these spaces, and derive Gaussian and multiplier bootstrap approximations for sums of high-dimensional functional time series. These results have numerous important statistical consequences. Exemplarily, we consider the developm...
• A quantitative Heppes theorem and multivariate Bernoulli distributions AI翻译
  作者：Fraiman, Ricardo; Moreno, Leonardo; Ransford, Thomas
  作者单位：Universidad de la Republica, Uruguay; Universidad de la Republica, Uruguay; Laval University
  摘要：Using some extensions of a theorem of Heppes on finitely supported discrete probability measures, we address the problems of classification and testing based on projections. In particular, when the support of the distributions is known in advance (as for instance for multivariate Bernoulli distributions), a single suitably chosen projection determines the distribution. Several applications of these results are considered.
• Estimating heterogeneous treatment effects with right-censored data via causal survival forests AI翻译
  作者：Cui, Yifan; Kosorok, Michael R.; Sverdrup, Erik; Wager, Stefan; Zhu, Ruoqing
  作者单位：Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Stanford University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perf...
• Estimating and improving dynamic treatment regimes with a time-varying instrumental variable AI翻译
  作者：Chen, Shuxiao; Zhang, Bo
  作者单位：University of Pennsylvania; Fred Hutchinson Cancer Center; Fred Hutchinson Cancer Center
  摘要：Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develop a framework of estimating properly defined 'optimal' DTRs with a time-varying instrumental variable (IV) when unmeasured covariates confound the treatment and outcome, rendering the potential outcome distributions only partially identified. We derive a novel Bellman equation under partial identification, use it to ...
• Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data AI翻译
  作者：Li, Xiudi; Li, Sijia; Luedtke, Alex
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either fully or partially observed out...