Journal of the Royal Statistical Society: Series B

最新文章

• Prediction sets for high-dimensional mixture of experts models AI翻译
  作者：Javanmard, Adel; Shao, Simeng; Bien, Jacob
  作者单位：University of Southern California; Amazon.com
  摘要：Large datasets make it possible to build predictive models that can capture heterogenous relationships between the response variable and features. The mixture of high-dimensional linear experts model posits that observations come from a mixture of high-dimensional linear regression models, where the mixture weights are themselves feature-dependent. In this article, we show how to construct valid prediction sets for an & ell;1-penalized mixture of experts model in the high-dimensional setting. ...
• Liyang Sun's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Sun, Liyang
  作者单位：University of London; University College London
• Supriya Tiwari and Pallavi Basu's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Tiwari, Supriya; Basu, Pallavi
  作者单位：Indian School of Business (ISB)
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• On the statistical analysis of grouped data: when Pearson χ2 and other divisible statistics are not goodness-of-fit AI翻译
  作者：Algeri, Sara; Khmaladze, Estate, V
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Victoria University Wellington
  摘要：Thousands of experiments are analysed, and papers are published each year involving the statistical analysis of grouped data. While this area of statistics is often perceived-somewhat naively-as saturated, several misconceptions still affect everyday practice, and new frontiers have so far remained unexplored. Researchers must be aware of the limitations affecting their analyses and what new possibilities are at their hands. The article introduces a unifying approach to the analysis of divisib...
• Quantifying individual risk for binary outcomes AI翻译
  作者：Wu, Peng; Ding, Peng; Geng, Zhi; Liu, Yue
  作者单位：Beijing Technology & Business University; University of California System; University of California Berkeley; Renmin University of China; Renmin University of China
  摘要：Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual risk, potentially leading to misleading results. This article fills this gap by examining...
• Gesine Reinert's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Reinert, Gesine
  作者单位：University of Oxford
• The causal effects of modified treatment policies under network interference AI翻译
  作者：Balkus, Salvador, V; Delaney, Scott W.; Hejazi, Nima S.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention-induced modified treatment policies-which we show identify such causal effects...
• Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models AI翻译
  作者：Ascolani, Filippo; Roberts, Gareth O.; Zanella, Giacomo
  作者单位：Duke University; University of Warwick; Bocconi University; Bocconi University
  摘要：We study general coordinate-wise Markov chain Monte Carlo schemes (such as Metropolis-within-Gibbs samplers), which are commonly used to fit Bayesian non-conjugate hierarchical models. We relate their convergence properties to the ones of the corresponding (potentially not implementable) random scan Gibbs sampler through the notion of conditional conductance. This allows us to study the performances of popular Metropolis-within-Gibbs schemes for non-conjugate hierarchical models, in high-dimen...
• Convexity and measures of statistical association AI翻译
  作者：Borgonovo, Emanuele; Figalli, Alessio; Ghosal, Promit; Plischke, Elmar; Savare, Giuseppe
  作者单位：Bocconi University; Bocconi University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Chicago; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
  摘要：Recent investigations on the measures of statistical association highlight essential properties such as zero-independence (the measure is zero if and only if the random variables are independent), monotonicity under information refinement, and max-functionality (the measure of association is maximal if and only if we are in the presence of a deterministic (noiseless) dependence). An open question concerns the reasons why measures of statistical associations satisfy one or more of those propert...
• Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation AI翻译
  作者：Tan, Linda S. L.
  作者单位：National University of Singapore
  摘要：Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in Gaussian variational approximation, natural gradient updates of the precision matrix do not ensure positive definiteness. To tackle this issue, we derive analytic natural gradient updates of the Cholesky factor of the covariance or precision matrix and consider sparsity constraints representing different posterior c...