538期

最新文章

• Causal Bounds for Outcome-Dependent Sampling in Observational Studies AI翻译
  作者：Gabriel, Erin E.; Sachs, Michael C.; Sjolander, Arvid
  作者单位：Karolinska Institutet
  摘要：Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs often suffer from unmeasured confounding, which generally precludes the nonparametric identification of causal effects. Nonparametric bounds can provide a way to narrow the range of possible values for a nonidentifiable causal effect without making additional untestable assumptions. The nonparametric bounds literature ...
• Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies AI翻译
  作者：Li, Zilin; Liu, Yaowu; Lin, Xihong
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Southwestern University of Finance & Economics - China; Harvard University
  摘要：We consider in this article detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a prespecified region, for example, a gene. In view of massive intergenic regions in whole genome sequenc...
• A Semiparametric Approach to Model Effect Modification AI翻译
  作者：Liang, Muxuan; Yu, Menggang
  作者单位：Fred Hutchinson Cancer Center; University of Wisconsin System; University of Wisconsin Madison
  摘要：One fundamental statistical question for research areas such as precision medicine and health disparity is about discovering effect modification of treatment or exposure by observed covariates. We propose a semiparametric framework for identifying such effect modification. Instead of using the traditional outcome models, we directly posit semiparametric models on contrasts, or expected differences of the outcome under different treatment choices or exposures. Through semiparametric estimation ...
• Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements AI翻译
  作者：Hassler, Gabriel; Tolkoff, Max R.; Allen, William L.; Ho, Lam Si Tung; Lemey, Philippe; Suchard, Marc A.
  作者单位：University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of California System; University of California Los Angeles; Swansea University; Dalhousie University; KU Leuven; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; Takeda Pharmaceutical Company Ltd; Takeda Pharmaceuticals International, Inc.
  摘要：Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typica...
• Robust Post-Matching Inference AI翻译
  作者：Abadie, Alberto; Spiess, Jann
  作者单位：Massachusetts Institute of Technology (MIT); Stanford University
  摘要：Nearest-neighbor matching is a popular nonparametric tool to create balance between treatment and control groups in observational studies. As a preprocessing step before regression, matching reduces the dependence on parametric modeling assumptions. In current empirical practice, however, the matching step is often ignored in the calculation of standard errors and confidence intervals. In this article, we show that ignoring the matching step results in asymptotically valid standard errors if m...
• Balancing Unobserved Covariates With Covariate-Adaptive Randomized Experiments AI翻译
  作者：Liu, Yang; Hu, Feifang
  作者单位：George Washington University
  摘要：Balancing important covariates is often critical in clinical trials and causal inference. Stratified permuted block (STR-PB) and covariate-adaptive randomization (CAR) procedures are widely used to balance observed covariates in practice. The balance properties of these procedures with respect to the observed covariates have been well studied. However, it has been questioned whether these methods will also yield a good balance for the unobserved covariates. In this article, we develop a genera...
• Warp Bridge Sampling: The Next Generation AI翻译
  作者：Wang, Lazhi; Jones, David E.; Meng, Xiao-Li
  作者单位：Texas A&M University System; Texas A&M University College Station; Harvard University
  摘要：Bridge sampling is an effective Monte Carlo (MC) method for estimating the ratio of normalizing constants of two probability densities, a routine computational problem in statistics, physics, chemistry, and other fields. The MC error of the bridge sampling estimator is determined by the amount of overlap between the two densities. In the case of unimodal densities, Warp-I, II, and III transformations are effective for increasing the initial overlap, but they are less so for multimodal densitie...
• Estimation of Low Rank High-Dimensional Multivariate Linear Models for Multi-Response Data AI翻译
  作者：Zou, Changliang; Ke, Yuan; Zhang, Wenyang
  作者单位：Nankai University; University System of Georgia; University of Georgia; University of York - UK
  摘要：In this article, we study low rank high-dimensional multivariate linear models (LRMLM) for high-dimensional multi-response data. We propose an intuitively appealing estimation approach and develop an algorithm for implementation purposes. Asymptotic properties are established to justify the estimation procedure theoretically. Intensive simulation studies are also conducted to demonstrate performance when the sample size is finite, and a comparison is made with some popular methods from the lit...
• The Hellinger Correlation AI翻译
  作者：Geenens, Gery; de Micheaux, Pierre Lafaye
  作者单位：University of New South Wales Sydney
  摘要：In this article, the defining properties of any valid measure of the dependence between two continuous random variables are revisited and complemented with two original ones, shown to imply other usual postulates. While other popular choices are proved to violate some of these requirements, a class of dependence measures satisfying all of them is identified. One particular measure, that we call the Hellinger correlation, appears as a natural choice within that class due to both its theoretical...
• Toward Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia AI翻译
  作者：Christiansen, Rune; Baumann, Matthias; Kuemmerle, Tobias; Mahecha, Miguel D.; Peters, Jonas
  作者单位：University of Copenhagen; Humboldt University of Berlin; Humboldt University of Berlin; Leipzig University; German Research Foundation (DFG); German Centre for Integrative Biodiversity Research (iDiv)
  摘要：How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates X on a real-valued response Y. We introduce a procedure for estimat...