537期

最新文章

• Separable Effects for Causal Inference in the Presence of Competing Events AI翻译
  作者：Stensrud, Mats J.; Young, Jessica G.; Didelez, Vanessa; Robins, James M.; Hernan, Miguel A.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Oslo; Harvard University; Harvard Medical School; Harvard Pilgrim Health Care; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
  摘要：In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatment effect on the event of interest not mediated by its effect on the competing event. The separable indirect effect is the treatment effect on the event of interest only through its effect on the competing event. Similar to Robins and Richar...
• Generalized Measures of Correlation for Asymmetry, Nonlinearity, and Beyond: Some Antecedents on Causality AI翻译
  作者：Allen, David E.; McAleer, Michael
  作者单位：University of Sydney; Asia University Taiwan; Edith Cowan University; Asia University Taiwan; University of Sydney; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Complutense University of Madrid; Complutense University of Madrid; University of Canterbury; Yokohama National University
  摘要：This note comments on the generalized measure of correlation (GMC) that was suggested by Zheng, Shi, and Zhang. The GMC concept was partly anticipated in some publications over 100 years earlier by Yule in the Proceedings of the Royal Society, and by Kendall. Other antecedents discussed include work on dependency by Renyi and Doksum and Samarov, together with the Yule-Simpson paradox. The GMC metric partly extends the concept of Granger causality, so that we consider causality, graphical analy...
• Rejoinder AI翻译
  作者：Chen, Rong; Yang, Dan; Zhang, Cun-Hui
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Hong Kong
• Dynamic Treatment Regimes: Statistical Methods for Precision Medicine AI翻译
  作者：Zhao, Ying-Qi
  作者单位：Fred Hutchinson Cancer Center
• Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data AI翻译
  作者：Yu, Jun; Wang, HaiYing; Ai, Mingyao; Zhang, Huiming
  作者单位：Beijing Institute of Technology; University of Connecticut; Peking University; Peking University; Peking University
  摘要：Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with replacement due to its high computational efficiency. If the data volume is so large that nonuniform subsampling probabilities cannot be calculated all at once, then subsampling with replacement is infeasible to implement. This article solves this problem using Poisson subsampling. We first derive optimal Poisson subs...
• Joint Structural Break Detection and Parameter Estimation in High-Dimensional Nonstationary VAR Models AI翻译
  作者：Safikhani, Abolfazl; Shojaie, Ali
  作者单位：State University System of Florida; University of Florida; University of Washington; University of Washington Seattle
  摘要：Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and solve it using a pe...
• Estimating and Accounting for Unobserved Covariates in High-Dimensional Correlated Data AI翻译
  作者：McKennan, Chris; Nicolae, Dan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Chicago
  摘要：Many high-dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These data, as well as nearly all high-throughput omic data, are influenced by technical and biological factors unknown to the researcher, which, if unaccounted for, can severely obfuscate estimation of and inference on the effects of interest. We therefore develo...
• Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks AI翻译
  作者：Bu, Fan; Aiello, Allison E.; Xu, Jason; Volfovsky, Alexander
  作者单位：Duke University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We propose a generative model and an inference scheme for epidemic processes on dynamic, adaptive contact networks. Network evolution is formulated as a link-Markovian process, which is then coupled to an individual-level stochastic susceptible-infectious-recovered model, to describe the interplay between the dynamics of the disease spread and the contact network underlying the epidemic. A Markov chain Monte Carlo framework is developed for likelihood-based inference from partial epidemic obse...
• From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation (vol 115, pg 138, 2020) AI翻译
  作者：Rosset, Saharon; Tibshirani, Ryan J.
  作者单位：Tel Aviv University; Carnegie Mellon University
• Mean and Covariance Estimation for Functional Snippets AI翻译
  作者：Lin, Zhenhua; Wang, Jane-Ling
  作者单位：National University of Singapore; University of California System; University of California Davis
  摘要：We consider estimation of mean and covariance functions of functional snippets, which are short segments of functions possibly observed irregularly on an individual specific subinterval that is much shorter than the entire study interval. Estimation of the covariance function for functional snippets is challenging since information for the far off-diagonal regions of the covariance structure is completely missing. We address this difficulty by decomposing the covariance function into a varianc...