Journal of the Royal Statistical Society: Series B

最新文章

• Statistical inference of the value function for reinforcement learning in infinite-horizon settings AI翻译
  作者：Shi, Chengchun; Zhang, Sheng; Lu, Wenbin; Song, Rui
  作者单位：University of London; London School Economics & Political Science; North Carolina State University
  摘要：Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision-making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper was to construct confidence intervals (CIs) for a policy's value in infinite horizon settings where the number of decision points diverges to infinity. We propose to model the action-value state function (Q-functio...
• Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model* AI翻译
  作者：Jiang, Feiyu; Zhao, Zifeng; Shao, Xiaofeng
  作者单位：Fudan University; University of Notre Dame; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We propose a piecewise linear quantile trend model to analyse the trajectory of the COVID-19 daily new cases (i.e. the infection curve) simultaneously across multiple quantiles. The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by COVID-19 infection curves) and...
• Andreas Buja, Richard A. Berk, Arun K. Kuchibhotla, Linda Zhao and Ed George's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Buja, Andreas; Berk, Richard A.; Kuchibhotla, Arun K.; Zhao, Linda; George, Ed
  作者单位：University of Pennsylvania; Simons Foundation; Flatiron Institute; University of Pennsylvania; Carnegie Mellon University
• Empirical Bayes PCA in high dimensions AI翻译
  作者：Zhong, Xinyi; Su, Chang; Fan, Zhou
  作者单位：Yale University; Yale University
  摘要：When the dimension of data is comparable to or larger than the number of data samples, principal components analysis (PCA) may exhibit problematic high-dimensional noise. In this work, we propose an empirical Bayes PCA method that reduces this noise by estimating a joint prior distribution for the principal components. EB-PCA is based on the classical Kiefer-Wolfowitz non-parametric maximum likelihood estimator for empirical Bayes estimation, distributional results derived from random matrix t...
• Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality AI翻译
  作者：Li, Sai; Cai, T. Tony; Li, Hongzhe
  作者单位：University of Pennsylvania; University of Pennsylvania
  摘要：This paper considers estimation and prediction of a high-dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models are available. When the set of informative auxiliary studies is known, an estimator and a predictor are proposed and their optimality is established. The optimal rates of convergence for prediction and estimation are faster than the correspondin...
• Fast increased fidelity samplers for approximate Bayesian Gaussian process regression AI翻译
  作者：Moran, Kelly R.; Wheeler, Matthew W.
  作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
  摘要：Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H-matrix approximations that scales at O(nlog2n). We show that this approximation's Kullback-Leibler divergence to the true posterior can be ma...
• Supervised multivariate learning with simultaneous feature auto-grouping and dimension reduction AI翻译
  作者：She, Yiyuan; Shen, Jiahui; Zhang, Chao
  作者单位：State University System of Florida; Florida State University; Peking University
  摘要：Modern high-dimensional methods often adopt the 'bet on sparsity' principle, while in supervised multivariate learning statisticians may face 'dense' problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modelling and relaxes the stringent sparsity assumption in v...
• Niwen Zhou and Xu Guo's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Zhou, Niwen; Guo, Xu
  作者单位：Beijing Normal University; Beijing Normal University
• Bayesian context trees: Modelling and exact inference for discrete time series AI翻译
  作者：Kontoyiannis, Ioannis; Mertzanis, Lambros; Panotopoulou, Athina; Papageorgiou, Ioannis; Skoularidou, Maria
  作者单位：University of Cambridge; Dartmouth College; University of Cambridge; University of Cambridge; MRC Biostatistics Unit
  摘要：We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg-orithm can compute the prior predictive likelihood exa-ctly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact p...
• Bayesian inference for risk minimization via exponentially tilted empirical likelihood AI翻译
  作者：Tang, Rong; Yang, Yun
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The celebrated Bernstein von-Mises theorem ensures credible regions from a Bayesian posterior to be well-calibrated when the model is correctly-specified, in the frequentist sense that their coverage probabilities tend to the nominal values as data accrue. However, this conventional Bayesian framework is known to lack robustness when the model is misspecified or partly specified, for example, in quantile regression, risk minimization based supervised/unsupervised learning and robust estimation...