Journal of the Royal Statistical Society: Series B

最新文章

• Generalized point process additive models AI翻译
  作者：Lee, Kuang-Yao; Sun, Jiehuan; Li, Bing; Li, Lexin
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Berkeley
  摘要：In this article, we propose a generalized point process additive model with a scalar response and high-dimensional point process predictors. Our proposal is built upon four key components: a realization of a point process as a random counting measure, a generalized point process regression framework, a new kernel function for random measure through kernel embedding, and a suite of low-dimensional structures including the additive model, reduced basis representation, and sparsity. We develop an...
• Combining evidence across filtrations† AI翻译
  作者：Choe, Yo Joong; Ramdas, Aaditya
  作者单位：INSEAD Business School; Carnegie Mellon University
  摘要：In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-processes in the same filtration can be combined effortlessly (by averaging), e-processes in different filtrations cannot because their validity ...
• Spectral change point estimation for high-dimensional time series by sparse tensor decomposition AI翻译
  作者：Zhang, Xinyu; Chan, Kung-Sik
  作者单位：East China Normal University; East China Normal University; University of Iowa
  摘要：Multivariate time series may be subject to partial structural changes over certain frequency band, for instance, in neuroscience. We study the change point detection problem with high-dimensional time series, within the framework of frequency domain. The overarching goal is to locate all change points and delineate which series are activated by the change, over which frequencies. In practice, the number of activated series per change and frequency could span from a few to full participation. W...
• Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures AI翻译
  作者：Chang, Ming-Chung
  作者单位：Academia Sinica - Taiwan
  摘要：Multi-stratum factorial designs, such as block designs and row-column designs, are widely used for screening treatment factors in experiments involving complex structures of experimental units due to multiple sources of error. In this study, we propose a unified model-free approach, termed orthogonalized moment aberration, to compare the similarities between level combinations of treatment factors assigned to heterogeneous experimental units. The proposed approach, which uses kernel functions ...
• Safaa K. Kadhem's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Kadhem, Safaa K.
  作者单位：Al-Muthanna University
• Michael Trosset's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whitely et al. AI翻译
  作者：Trosset, Michael W.
  作者单位：Indiana University Bloomington; Indiana University System; Indiana University Bloomington
• Zihao Wen and David L. Dowe's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Wen, Zihao; Dowe, David L.
  作者单位：Monash University; South China Agricultural University
• Anytime validity is free: inducing sequential tests AI翻译
  作者：Koning, Nick W.; Van Meer, Sam
  作者单位：Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
  摘要：Anytime valid sequential tests permit us to stop testing based on the current data, without invalidating the inference. Given a maximum number of observations N, one may believe this must come at the cost of power when compared to a conventional test that waits until all N observations have arrived. Our first contribution is to show that this is false: for any valid test based on N observations, we show how to construct an anytime valid sequential test that matches it after N observations. Our...
• DecoR: deconfounding time series with robust regression AI翻译
  作者：Schur, Felix; Blieske, Pio; Peters, Jonas
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency domain. We introduce Deconfou...
• Principal stratification with continuous post-treatment variables: nonparametric identification and semiparametric estimation AI翻译
  作者：Lu, Sizhu; Jiang, Zhichao; Ding, Peng
  作者单位：University of California System; University of California Berkeley; Sun Yat Sen University
  摘要：Post-treatment variables often complicate causal inference. They appear in many scientific problems, including non-compliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy to address these challenges by adjusting for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the mechanism of the treatment's im...