2021

最新文章

• Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists AI翻译
  作者：Chan, Lax; Silverman, Bernard W.; Vincent, Kyle
  作者单位：University of Nottingham
  摘要：Multiple systems estimation strategies have recently been applied to quantify hard-to-reach populations, particularly when estimating the number of victims of human trafficking and modern slavery. In such contexts, it is not uncommon to see sparse or even no overlap between some of the lists on which the estimates are based. These create difficulties in model fitting and selection, and we develop inference procedures to address these challenges. The approach is based on Poisson log-linear regr...
• Discussion of Exponential-Family Embedding With Application to Cell Developmental Trajectories for Single-Cell RNA-Seq Data AI翻译
  作者：Hu, Jian; Li, Mingyao
  作者单位：University of Pennsylvania
• Discussion of Kallus and Mo, Qi, and Liu: New Objectives for Policy Learning AI翻译
  作者：Vansteelandt, Stijn; Dukes, Oliver
  作者单位：Ghent University; University of London; London School of Hygiene & Tropical Medicine
• Auto-G-Computation of Causal Effects on a Network AI翻译
  作者：Tchetgen Tchetgen, Eric J.; Fulcher, Isabel R.; Shpitser, Ilya
  作者单位：University of Pennsylvania; Harvard University; Harvard Medical School; Johns Hopkins University
  摘要：Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The ...
• Predictive Inference for Locally Stationary Time Series With an Application to Climate Data AI翻译
  作者：Das, Srinjoy; Politis, Dimitris N.
  作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：The model-free prediction principle of Politis has been successfully applied to general regression problems, as well as problems involving stationary time series. However, with long time series, for example, annual temperature measurements spanning over 100 years or daily financial returns spanning several years, it may be unrealistic to assume stationarity throughout the span of the dataset. In this article, we show how model-free prediction can be applied to handle time series that are only ...
• Introduction to the Special Section on Synthetic Control Methods AI翻译
  作者：Abadie, Alberto; Cattaneo, Matias D.
• Variational Bayes for high-dimensional linear regression with sparse priors (Jan, 10.1080/01621459.2020.1847121, 2021) AI翻译
  作者：Ray, Kolyan; Szabo, Botond
• Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach AI翻译
  作者：Ma, Cong; Lu, Junwei; Liu, Han
  作者单位：Princeton University; Harvard University; Harvard T.H. Chan School of Public Health; Northwestern University; Northwestern University
  摘要：Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between brain regions under natural stimuli. We propose a modeling framework for ISA that is based on Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of a...
• Bayesian Structure Learning in Multilayered Genomic Networks AI翻译
  作者：Ha, Min Jin; Stingo, Francesco Claudio; Baladandayuthapani, Veerabhadran
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Florence; University of Michigan System; University of Michigan
  摘要：Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model t...
• Principal Component Analysis of Spatially Indexed Functions AI翻译
  作者：Kuenzer, Thomas; Hormann, Siegfried; Kokoszka, Piotr
  作者单位：Graz University of Technology; Colorado State University System; Colorado State University Fort Collins
  摘要：We develop an expansion, similar in some respects to the Karhunen-Loeve expansion, but which is more suitable for functional data indexed by spatial locations on a grid. Unlike the traditional Karhunen-Loeve expansion, it takes into account the spatial dependence between the functions. By doing so, it provides a more efficient dimension reduction tool, both theoretically and in finite samples, for functional data with moderate spatial dependence. For such data, it also possesses other theoreti...