Journal of the Royal Statistical Society: Series B

最新文章

• Anchor regression: Heterogeneous data meet causality AI翻译
  作者：Rothenhausler, Dominik; Meinshausen, Nicolai; Buhlmann, Peter; Peters, Jonas
  作者单位：Stanford University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen
  摘要：We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many variables are affected by interventions or only some variables are affected, but the perturbations are strong. If the training and test distributions differ by a shift, causal parameters might be too conservative to perform well on the above task....
• Instrument residual estimator for any response variable with endogenous binary treatment* AI翻译
  作者：Lee, Myoung-jae
  作者单位：Korea University
  摘要：Given an endogenous/confounded binary treatment D, a response Y with its potential versions (Y-0, Y-1) and covariates X, finding the treatment effect is difficult if Y is not continuous, even when a binary instrumental variable (IV) Z is available. We show that, for any form of Y (continuous, binary, mixed, horizontal ellipsis ), there exists a decomposition Y = mu(0)(X) + mu(1)(X)D + error with E(error|Z,X) = 0, where mu 1(X)equivalent to E(Y1-Y0|complier,X) and 'compliers' are those who get ...
• Nonparametric density estimation over complicated domains AI翻译
  作者：Ferraccioli, Federico; Arnone, Eleonora; Finos, Livio; Ramsay, James O.; Sangalli, Laura M.
  作者单位：University of Padua; Polytechnic University of Milan; University of Padua; McGill University
  摘要：We propose a nonparametric method for density estimation over (possibly complicated) spatial domains. The method combines a likelihood approach with a regularization based on a differential operator. We demonstrate the good inferential properties of the method. Moreover, we develop an estimation procedure based on advanced numerical techniques, and in particular making use of finite elements. This ensures high computational efficiency and enables great flexibility. The proposed method efficien...
• Use of model reparametrization to improve variational Bayes† AI翻译
  作者：Tan, Linda S. L.
  作者单位：National University of Singapore
  摘要：We propose using model reparametrization to improve variational Bayes inference for hierarchical models whose variables can be classified as global (shared across observations) or local (observation-specific). Posterior dependence between local and global variables is minimized by applying an invertible affine transformation on the local variables. The functional form of this transformation is deduced by approximating the posterior distribution of each local variable conditional on the global ...
• Analysis of networks via the sparse β-model AI翻译
  作者：Chen, Mingli; Kato, Kengo; Leng, Chenlei
  作者单位：University of Warwick; Cornell University; University of Warwick
  摘要：Data in the form of networks are increasingly available in a variety of areas, yet statistical models allowing for parameter estimates with desirable statistical properties for sparse networks remain scarce. To address this, we propose the Sparse beta-Model (S beta M), a new network model that interpolates the celebrated Erdos-Renyi model and the beta-model that assigns one different parameter to each node. By a novel reparameterization of the beta-model to distinguish global and local paramet...
• Joint quantile regression for spatial data AI翻译
  作者：Chen, Xu; Tokdar, Surya T.
  作者单位：Duke University
  摘要：Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analysing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (Journal ...
• Discussion on the paper titled Gaussian differential privacy AI翻译
  作者：Wijayatunga, Priyantha
  作者单位：Umea University
• High-dimensional ordinary least-squares projection for screening variables (vol 78, pg 589, 2016) AI翻译
  作者：Wang, Xiangyu; Leng, Chenlei; Boot, Tom
  作者单位：Alphabet Inc.; Google Incorporated; University of Warwick; University of Groningen
• RSS Written Discussion for December 16, 2020 AI翻译
  作者：Chai, Christine P.
  作者单位：Microsoft
• The confidence interval method for selecting valid instrumental variables AI翻译
  作者：Windmeijer, Frank; Liang, Xiaoran; Hartwig, Fernando P.; Bowden, Jack
  作者单位：University of Oxford; University of Oxford; University of Bristol; University of Bristol; University of Exeter
  摘要：We propose a new method, the confidence interval (CI) method, to select valid instruments from a larger set of potential instruments for instrumental variable (IV) estimation of the causal effect of an exposure on an outcome. Invalid instruments are such that they fail the exclusion conditions and enter the model as explanatory variables. The CI method is based on the CIs of the per instrument causal effects estimates and selects the largest group with all CIs overlapping with each other as th...