5期

最新文章

• Christian Hennig's contribution to the Discussion of 'Safe testing' by Grunwald, de Heide, and Koolen AI翻译
  作者：Hennig, Christian
  作者单位：University of Bologna
• David Draper and Erdong Guo's contribution to the discussion of 'Martingale posterior distributions', by Fong, Holmes and Walker AI翻译
  作者：Draper, David; Guo, Erdong
  作者单位：University of California System; University of California Santa Cruz; University of California System; University of California Santa Cruz
• Two-way dynamic factor models for high-dimensional matrix-valued time series AI翻译
  作者：Yuan, Chaofeng; Gao, Zhigen; He, Xuming; Huang, Wei; Guo, Jianhua
  作者单位：Northeast Normal University - China; Northeast Normal University - China; Heilongjiang University; Heilongjiang University; Northeast Normal University - China; Washington University (WUSTL); Beijing Technology & Business University
  摘要：In this article, we introduce a two-way dynamic factor model (2w-DFM) for high-dimensional matrix-valued time series and study some of the basic theoretical properties in terms of identifiability and estimation accuracy. The proposed model aims to capture separable and low-dimensional effects of row and column attributes and their correlations across rows, columns, and time points. Complementary to other dynamic factor models for high-dimensional data, the 2w-DFM inherits the dimension-reducti...
• Semi-parametric estimation of treatment effects in randomised experiments AI翻译
  作者：Athey, Susan; Bickel, Peter J.; Chen, Aiyou; Imbens, Guido W.; Pollmann, Michael
  作者单位：Stanford University; National Bureau of Economic Research; University of California System; University of California Berkeley; Alphabet Inc.; Google Incorporated; Stanford University; Duke University
  摘要：We develop new semi-parametric methods for estimating treatment effects. We focus on settings where the outcome distributions may be thick tailed, where treatment effects may be small, where sample sizes are large, and where assignment is completely random. This setting is of particular interest in recent online experimentation. We propose using parametric models for the treatment effects, leading to semi-parametric models for the outcome distributions. We derive the semi-parametric efficiency...
• David R. Bickel's contribution to the Discussion of 'Safe testing' by Grunwald, De Heide, and Koolen AI翻译
  作者：Bickel, David R.
  作者单位：University of North Carolina; University of North Carolina Greensboro
• Martingale posterior distributions AI翻译
  作者：Fong, Edwin; Holmes, Chris; Walker, Stephen G.
  作者单位：Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin; University of Oxford
  摘要：The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we present a different perspective that focuses on missing observations as the source of statistical uncertainty, with the parameter of interest being known precisely given the entire population. We argue that the foundation of Bayesian inference is to assign a distribution on missing observations conditional on what has been observed. In the i.i.d. setting with an observed sample of size n, the Bayesia...
• Stationary nonseparable space-time covariance functions on networks AI翻译
  作者：Porcu, Emilio; White, Philip A.; Genton, Marc G.
  作者单位：Khalifa University of Science & Technology; Berry Consultants, LLC; Brigham Young University; King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
  摘要：The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalised network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different temporal instants. We provide new classes of stationary nonseparable space-time covariance functions where space can be a generalised netw...
• Christine P. Chai's contribution to the Discussion of 'Safe testing' by Grünwald, De Heide, and Koolen AI翻译
  作者：Chai, Christine P.
  作者单位：Microsoft
  摘要：We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes. Tests based on e-values are safe, i.e. they preserve type-I error guarantees, under such optional continuation. We define growth rate optimality (GAO) as an analogue of power in an optional continuation context, and we show ...
• Alexander Ly's contribution to the Discussion of 'Safe testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Ly, Alexander
• A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference AI翻译
  作者：Atchade, Yves; Wang, Liwei
  作者单位：Boston University; Boston University
  摘要：We propose a very fast approximate Markov chain Monte Carlo sampling framework that is applicable to a large class of sparse Bayesian inference problems. The computational cost per iteration in several regression models is of order O(n(s+J)), where n is the sample size, s is the underlying sparsity of the model, and J is the size of a randomly selected subset of regressors. This cost can be further reduced by data sub-sampling when stochastic gradient Langevin dynamics are employed. The algori...