Journal of the Royal Statistical Society: Series B

最新文章

• Seconder 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：Pensky, Marianna
  作者单位：State University System of Florida; University of Central Florida
• Two-stage estimation and bias-corrected empirical likelihood in a partially linear single-index varying-coefficient model AI翻译
  作者：Xue, Liugen
  作者单位：Henan University
  摘要：The estimation and empirical likelihood (EL) of the parameters of interest in a partially linear single-index varying-coefficient model are studied. A two-stage method is presented to estimate the regression parameters and the coefficient functions. The asymptotic distributions of the proposed estimators are obtained. Meanwhile, a bias-corrected EL ratio for the regression parameters is proposed. It is shown that the ratio is asymptotically standard chi-squared. The result can be directly used...
• 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...
• Vintage factor analysis with Varimax performs statistical inference AI翻译
  作者：Rohe, Karl; Zeng, Muzhe
  摘要：In the 1930s, Psychologists began developing Multiple-Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In this form of factor analysis, the Varimax factor rotation redraws the axes through the multi-dimensional factors to make them sparse and thus make them more interpretable. Charles Spearman and many others objected to factor rotations because the factors seem to be rotationally invariant. Despite ...
• Konstantin Siroki and Korbinian Strimmer's contribution to the Discussion of 'Vintage factor analysis with varimax performs statistical inference' by Rohe and Zeng AI翻译
  作者：Siroki, Konstantin; Strimmer, Korbinian
  作者单位：University of Manchester
• Yang Liu's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Liu, Yang
  作者单位：University System of Maryland; University of Maryland College Park
• Normalised latent measure factor models AI翻译
  作者：Beraha, Mario; Griffin, Jim E.
  作者单位：University of Turin; University of London; University College London
  摘要：We propose a methodology for modelling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalised random measures, we consider a prior distribution for a collection of discrete random measures where each measure is a linear combination of a set of latent measures, interpretable as characteristic traits shared by different distributions, with positive random weights. The model is nonidentified and a method for postprocessing posterior sam...
• Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution AI翻译
  作者：Li, Wei; Miao, Wang; Tchetgen, Eric Tchetgen
  作者单位：Renmin University of China; Renmin University of China; Peking University; University of Pennsylvania
  摘要：We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for vn-estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires ...
• Blair Bilodeau's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Bilodeau, Blair
  作者单位：University of Toronto
• Mats J Stensrud and Aaron L. Sarvet's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Stensrud, Mats J.; Sarvet, Aaron L.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne