534期

最新文章

• 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 ...
• 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...
• Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks AI翻译
  作者：Forastiere, Laura; Airoldi, Edoardo M.; Mealli, Fabrizia
  作者单位：Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Florence
  摘要：Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other units, such as their neighbors in the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended-say, to include the treatment of neighbors, and...
• Permutation Tests for Infection Graphs AI翻译
  作者：Khim, Justin; Loh, Po-Ling
  作者单位：Carnegie Mellon University; University of Wisconsin System; University of Wisconsin Madison; Columbia University
  摘要：We formulate and analyze a novel hypothesis testing problem for inferring the edge structure of an infection graph. In our model, a disease spreads over a network via contagion or random infection, where the times between successive contagion events are independent exponential random variables with unknown rate parameters. A subset of nodes is also censored uniformly at random. Given the observed infection statuses of nodes in the network, the goal is to determine the underlying graph. We pres...
• Inference for Multivariate Regression Model Based on Synthetic Data Generated Using Plug-in Sampling AI翻译
  作者：Moura, Ricardo; Klein, Martin; Zylstra, John; Coelho, Carlos A.; Sinha, Bimal
  作者单位：Universidade Nova de Lisboa; US Food & Drug Administration (FDA); University System of Maryland; University of Maryland Baltimore County; Universidade Nova de Lisboa
  摘要：In this article, the authors derive the likelihood-based exact inference for singly and multiply imputed synthetic data in the context of a multivariate regression model. The synthetic data are generated via the Plug-in Sampling method, where the unknown parameters in the model are set equal to the observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Simulation studies are carried out in order to confirm th...
• Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies AI翻译
  作者：Lee, Kwonsang; Small, Dylan S.; Dominici, Francesca
  作者单位：Sungkyunkwan University (SKKU); University of Pennsylvania; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this article, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses randomization-based tests to as...