Journal of the American Statistical Association

最新文章

• Kernel-Based Partial Permutation Test for Detecting Heterogeneous Functional Relationship AI翻译
  作者：Li, Xinran; Jiang, Bo; Liu, Jun S.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Harvard University
  摘要：We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector Y on leading principle components of a kernel matrix fixed and permute Y's projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of function...
• Latent Gaussian Count Time Series AI翻译
  作者：Jia, Yisu; Kechagias, Stefanos; Livsey, James; Lund, Robert; Pipiras, Vladas
  作者单位：State University System of Florida; University of North Florida; SAS Institute Inc; University of California System; University of California Santa Cruz; University of North Carolina; University of North Carolina Chapel Hill
  摘要：This article develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any prespecified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule-Walker estimation paradigms, ba...
• Experimental Evaluation of Individualized Treatment Rules AI翻译
  作者：Imai, Kosuke; Li, Michael Lingzhi
  作者单位：Harvard University; Harvard University; Massachusetts Institute of Technology (MIT)
  摘要：The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a target population. We propose a new evaluation metric, the population average prescriptive effect (PAPE). The PAPE compares the performance of ITR with that of non-individualized treatment rule, which randomly treats the same pro...
• Discussion of A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models by Chenguang Dai, Buyu Lin, Xin Xing, and Jun S. Liu AI翻译
  作者：Janson, Lucas
  作者单位：Harvard University
• Discussion on: A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models by Dai, Lin, Zing, Liu AI翻译
  作者：Claeskens, Gerda; Jansen, Maarten; Zhou, Jing
  作者单位：KU Leuven; Universite Libre de Bruxelles; Universite Libre de Bruxelles; University of East Anglia; KU Leuven
• The Maximum of the Periodogram of a Sequence of Functional Data AI翻译
  作者：Cerovecki, Clement; Characiejus, Vaidotas; Hoermann, Siegfried
  作者单位：KU Leuven; Universite Libre de Bruxelles; University of Southern Denmark; Graz University of Technology
  摘要：We study the periodogram operator of a sequence of functional data. Using recent advances in Gaussian approximation theory, we derive the asymptotic distribution of the maximum norm over all fundamental frequencies. We consider the case where the noise variables are independent and then generalize our results to functional linear processes. Our theory can be used for detecting periodic signals in functional time series when the length of the period is unknown. We demonstrate the proposed metho...
• Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances AI翻译
  作者：Li, Runze; Xu, Kai; Zhou, Yeqing; Zhu, Liping
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Anhui Normal University; Tongji University; Renmin University of China; Renmin University of China
  摘要：In this article, we test for the effects of high-dimensional covariates on the response. In many applications, different components of covariates usually exhibit various levels of variation, which is ubiquitous in high-dimensional data. To simultaneously accommodate such heteroscedasticity and high dimensionality, we propose a novel test based on an aggregation of the marginal cumulative covariances, requiring no prior information on the specific form of regression models. Our proposed test st...
• Network Structure Learning Under Uncertain Interventions AI翻译
  作者：Castelletti, Federico; Peluso, Stefano
  作者单位：Catholic University of the Sacred Heart; University of Milano-Bicocca
  摘要：Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the network of dependencies among variables, a task which is of primary interest in many fields and specifically in biology. Different DAGs may encode equivalent conditional independence structures, implying limited ability, with observational data, to identify causal relations. In many contexts however, measurements are collected under heterogeneous settings where variables are subject to exogenous interventions. I...
• Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration AI翻译
  作者：Wang, Tianhao; Ratcliffe, Sarah J.; Guo, Wensheng
  作者单位：Rush University; University of Virginia; University of Pennsylvania
  摘要：In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-spe...
• Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference AI翻译
  作者：Wu, Ruijia; Zhang, Linjun; Cai, T. Tony
  作者单位：University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick
  摘要：Sparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed and their theoretical properties are investigated. Both minimax upper and lower bounds are established and the results show that the proposed algorithms are rate-optimal, up to a logarithmic factor. Moreover, a refitting algorithm is proposed to establish as...