5期

最新文章

• Glenn Shafer's contribution to the Discussion of 'Safe testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Shafer, Glenn
  作者单位：Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick
• Vladimir Vovk's contribution to the Discussion of 'Safe testing' by Grunwald, de Heide, and Koolen AI翻译
  作者：Vovk, Vladimir
  作者单位：University of London; Royal Holloway University London
• Nonparametric measure-transportation-based methods for directional data AI翻译
  作者：Hallin, M.; Liu, H.; Verdebout, T.
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：This article proposes various nonparametric tools based on measure transportation for directional data. We use optimal transports to define new notions of distribution and quantile functions on the hypersphere, with meaningful quantile contours and regions and closed-form formulas under the classical assumption of rotational symmetry. The empirical versions of our distribution functions enjoy the expected Glivenko-Cantelli property of traditional distribution functions. They provide fully dist...
• Neil Dey, Ryan Martin, and Jonathan P. Williams' contribution to the Discussion of 'Safe Testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Dey, Neil; Martin, Ryan; Williams, Jonathan P.
  作者单位：North Carolina State University
• Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation AI翻译
  作者：Tan, Zhiqiang
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds and doubly robust estimating functions. We also derive new, relaxed population bounds, depending on weighted linear outcome quantile regression. At the sample level, we develop new methods and theory for obtaining not only doubly robust point estimators for th...
• David Rossell's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Rossell, David
  作者单位：Pompeu Fabra University; Pompeu Fabra University
• Safe testing AI翻译
  作者：Pace, Luigi; Salvan, Alessandra
  作者单位：University of Udine; University of Padua
  摘要：We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes. Tests based on e-values are safe, i.e. they preserve type-I error guarantees, under such optional continuation. We define growth rate optimality (GAO) as an analogue of power in an optional continuation context, and we show ...
• Sandwich boosting for accurate estimation in partially linear models for grouped data AI翻译
  作者：Young, Elliot H.; Shah, Rajen D.
  作者单位：University of Cambridge
  摘要：We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear model parameters through weighted least squares, with optimal weights (given by the inverse covariance of the response, conditional on the covariates) typically estimated by maximizing a (restricted) likelihood from random effects modelling or by using generalized estimating equations. We introduce a new 'sandwich loss' ...
• Thorsten Dickhaus's contribution to the Discussion of 'Safe testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Dickhaus, Thorsten
  作者单位：Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen
• Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model AI翻译
  作者：Celentano, Michael; Montanari, Andrea
  作者单位：University of California System; University of California Berkeley; Stanford University; Stanford University
  摘要：We consider the problem of estimating a low-dimensional parameter in high-dimensional linear regression. Constructing an approximately unbiased estimate of the parameter of interest is a crucial step towards performing statistical inference. Several authors suggest to orthogonalize both the variable of interest and the outcome with respect to the nuisance variables, and then regress the residual outcome with respect to the residual variable. This is possible if the covariance structure of the ...