Journal of the American Statistical Association

最新文章

• Theory of Statistical Inference AI翻译
  作者：Mukherjee, Somabha
  作者单位：National University of Singapore
• Bayesian Integrative Region Segmentation in Spatially Resolved Transcriptomic Studies AI翻译
  作者：Yan, Yinqiao; Luo, Xiangyu
  作者单位：Renmin University of China
  摘要：The spatially resolved transcriptomic study is a recently developed biological experiment that can measure gene expressions and retain spatial information simultaneously, opening a new avenue to characterize fine-grained tissue structures. In this article, we propose a nonparametric Bayesian method named BINRES to carry out the region segmentation for a tissue section by integrating all the three types of data generated during the study-gene expressions, spatial coordinates, and the histology ...
• Robust Validation: Confident Predictions Even When Distributions Shift AI翻译
  作者：Cauchois, Maxime; Gupta, Suyash; Ali, Alnur; Duchi, John C.
  作者单位：Stanford University; Stanford University
  摘要：While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy-coming from robust statistics and optimization-is thus to build a model robust to distributional perturbations. In this article, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present ...
• Controlled Epidemiological Studies AI翻译
  作者：Ghosh, Kaushik
  作者单位：Nevada System of Higher Education (NSHE); University of Nevada Las Vegas
• In Nonparametric and High-Dimensional Models, Bayesian Ignorability is an Informative Prior AI翻译
  作者：Linero, Antonio R.
  作者单位：University of Texas System; University of Texas Austin
  摘要：In problems with large amounts of missing data one must model two distinct data generating processes: the outcome process, which generates the response, and the missing data mechanism, which determines the data we observe. Under the ignorability condition of Rubin, however, likelihood-based inference for the outcome process does not depend on the missing data mechanism so that only the former needs to be estimated; partially because of this simplification, ignorability is often used as a basel...
• Factor Modeling for Clustering High-Dimensional Time Series AI翻译
  作者：Zhang, Bo; Pan, Guangming; Yao, Qiwei; Zhou, Wang
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Nanyang Technological University; University of London; London School Economics & Political Science; National University of Singapore
  摘要：We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific fac...
• A General M-estimation Theory in Semi-Supervised Framework AI翻译
  作者：Song, Shanshan; Lin, Yuanyuan; Zhou, Yong
  作者单位：Chinese University of Hong Kong; East China Normal University; East China Normal University
  摘要：We study a class of general M-estimators in the semi-supervised setting, wherein the data are typically a combination of a relatively small labeled dataset and large amounts of unlabeled data. A new estimator, which efficiently uses the useful information contained in the unlabeled data, is proposed via a projection technique. We prove consistency and asymptotic normality, and provide an inference procedure based on K -fold cross-validation. The optimal weights are derived to balance the contr...
• Guaranteed Functional Tensor Singular Value Decomposition AI翻译
  作者：Han, Rungang; Shi, Pixu; Zhang, Anru R.
  作者单位：Duke University; Duke University; Duke University; Duke University
  摘要：This article introduces the functional tensor singular value decomposition (FTSVD), a novel dimension reduction framework for tensors with one functional mode and several tabular modes. The problem is motivated by high-order longitudinal data analysis. Our model assumes the observed data to be a random realization of an approximate CP low-rank functional tensor measured on a discrete time grid. Incorporating tensor algebra and the theory of reproducing kernel Hilbert space (RKHS), we propose a...
• Statistical Inference of Cell-Type Proportions Estimated from Bulk Expression Data AI翻译
  作者：Cai, Biao; Zhang, Jingfei; Li, Hongyu; Su, Chang; Zhao, Hongyu
  作者单位：City University of Hong Kong; Emory University; Yale University; Emory University
  摘要：There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many methods have been proposed recently, quantifying the uncertainties associated with the estimated cell-type proportions has not been well studied. Lack of consideration of these uncertainties can lead to missed or false findings in downstream analyses. In thi...
• Joint Tensor Modeling of Single Cell 3D Genome and Epigenetic Data with Muscle AI翻译
  作者：Park, Kwangmoon; Keles, Sunduz
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of three-dimensional genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing, methods that can jointly analyze multiple single cell modalities with scHi-C data are lacki...