Journal of the Royal Statistical Society: Series B

最新文章

• Michael Lavine and James Hodges' contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Lavine, Michael; Hodges, James
  作者单位：University of Massachusetts System; University of Massachusetts Amherst; University of Minnesota System; University of Minnesota Twin Cities
• Synthetic controls with staggered adoption AI翻译
  作者：Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse
  作者单位：Harvard University; University of California System; University of California Berkeley
  摘要：Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units that closely balances the treated unit's pre-treatment outcomes. In this paper, we generalize SCM, originally designed to study a single treated unit,...
• Inferential Wasserstein generative adversarial networks AI翻译
  作者：Chen, Yao; Gao, Qingyi; Wang, Xiao
  作者单位：Purdue University System; Purdue University
  摘要：Generative adversarial networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player training of GANs but has other defects such as mode collapse and lack of metric to detect the convergence. We introduce a novel inferential Wasserstein GAN (iWGAN) model, which is a principled framework to fuse autoencoders and WGANs. The iWGAN model jointly lea...
• Heather Battey's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Battey, Heather
  作者单位：Imperial College London
• Multiply robust estimation of causal effects under principal ignorability AI翻译
  作者：Jiang, Zhichao; Yang, Shu; Ding, Peng
  作者单位：University of Massachusetts System; University of Massachusetts Amherst; North Carolina State University; University of California System; University of California Berkeley
  摘要：Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stratification characterizes such a mechanism by targeting subgroup causal effects within principal strata, which are defined by the joint potential values of an intermediate variable. Due to the fundamental problem of causal inference, principal strata are inherently latent, rendering it challenging to identify and esti...
• Efficient evaluation of prediction rules in semi-supervised settings under stratified sampling AI翻译
  作者：Gronsbell, Jessica; Liu, Molei; Tian, Lu; Cai, Tianxi
  作者单位：University of Toronto; Harvard University; Stanford University; Harvard University; Harvard Medical School
  摘要：In many contemporary applications, large amounts of unlabelled data are readily available while labelled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabelled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labelled data are selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly...
• Waste-free sequential Monte Carlo AI翻译
  作者：Dau, Hai-Dang; Chopin, Nicolas
  作者单位：Institut Polytechnique de Paris; ENSAE Paris; Institut Polytechnique de Paris
  摘要：A standard way to move particles in a sequential Monte Carlo (SMC) sampler is to apply several steps of a Markov chain Monte Carlo (MCMC) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asympto...
• Oliver Hines and Karla Diaz-Ordaz's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Hines, Oliver; Diaz-Ordaz, Karla
  作者单位：University of London; London School of Hygiene & Tropical Medicine
• High-dimensional, multiscale online changepoint detection AI翻译
  作者：Chen, Yudong; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; University of London; London School Economics & Political Science; University of London; University College London
  摘要：We introduce a new method for high-dimensional, online changepoint detection in settings where a p-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that both its storage requirements and worstcase computational complexity per new observation are independent ...
• Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators AI翻译
  作者：Zhou, Xiang
  作者单位：Harvard University
  摘要：Causal mediation analysis concerns the pathways through which a treatment affects an outcome. While most of the mediation literature focuses on settings with a single mediator, a flourishing line of research has examined settings involving multiple mediators, under which path-specific effects (PSEs) are often of interest. We consider estimation of PSEs when the treatment effect operates through K(>= 1) causally ordered, possibly multivariate mediators. In this setting, the PSEs for many causal...