2021

最新文章

• An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls AI翻译
  作者：Chernozhukov, Victor; Wuthrich, Kaspar; Zhu, Yinchu
  作者单位：Massachusetts Institute of Technology (MIT); University of California System; University of California San Diego; Brandeis University
  摘要：We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against missp...
• The Effects of Stringent and Mild Interventions for Coronavirus Pandemic AI翻译
  作者：Tian, Ting; Tan, Jianbin; Luo, Wenxiang; Jiang, Yukang; Chen, Minqiong; Yang, Songpan; Wen, Canhong; Pan, Wenliang; Wang, Xueqin
  作者单位：Sun Yat Sen University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatme...
• More Efficient Policy Learning via Optimal Retargeting AI翻译
  作者：Kallus, Nathan
  作者单位：Cornell University; Cornell University
  摘要：Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned policies. We study a solution to this problem based on retargeting, that is, changing the population on which policies are optimized. We first argue that at the population level,...
• Integrating Multidimensional Data for Clustering Analysis With Applications to Cancer Patient Data AI翻译
  作者：Park, Seyoung; Xu, Hao; Zhao, Hongyu
  作者单位：Sungkyunkwan University (SKKU); Yale University
  摘要：Advances in high-throughput genomic technologies coupled with large-scale studies including The Cancer Genome Atlas (TCGA) project have generated rich resources of diverse types of omics data to better understand cancer etiology and treatment responses. Clustering patients into subtypes with similar disease etiologies and/or treatment responses using multiple omics data types has the potential to improve the precision of clustering than using a single data type. However, in practice, patient c...
• Individualized Multidirectional Variable Selection AI翻译
  作者：Tang, Xiwei; Xue, Fei; Qu, Annie
  作者单位：University of Virginia; University of Pennsylvania
  摘要：In this article, we propose a heterogeneous modeling framework which achieves individual-wise feature selection and heterogeneous covariates' effects subgrouping simultaneously. In contrast to conventional model selection approaches, the new approach constructs a separation penalty with multidirectional shrinkages, which facilitates individualized modeling to distinguish strong signals from noisy ones and selects different relevant variables for different individuals. Meanwhile, the proposed m...
• Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models AI翻译
  作者：Ma, Rong; Cai, T. Tony; Li, Hongzhe
  作者单位：University of Pennsylvania; University of Pennsylvania
  摘要：High-dimensional logistic regression is widely used in analyzing data with binary outcomes. In this article, global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression settings. A test statistic for testing the global null hypothesis is constructed using a generalized low-dimensional projection for bias correction and its asymptotic null distribution is derived. A lower bound for the global testing is established, which sho...
• Sampling Algorithms for Discrete Markov Random Fields and Related Graphical Models AI翻译
  作者：Izenman, Alan Julian
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：Discrete Markov random fields are undirected graphical models in which the nodes of a graph are discrete random variables with values usually represented by colors. Typically, graphs are taken to be square lattices, although more general graphs are also of interest. Such discrete MRFs have been studied in many disciplines. We describe the two most popular types of discrete MRFs, namely the two-state Ising model and the q-state Potts model, and variations such as the cellular automaton, the cel...
• A Gibbs Sampler for a Class of Random Convex Polytopes AI翻译
  作者：Jacob, Pierre E.; Gong, Ruobin; Edlefsen, Paul T.; Dempster, Arthur P.
  作者单位：Harvard University; Rutgers University System; Rutgers University New Brunswick; Fred Hutchinson Cancer Center
  摘要：We present a Gibbs sampler for the Dempster-Shafer (DS) approach to statistical inference for categorical distributions. The DS framework extends the Bayesian approach, allows in particular the use of partial prior information, and yields three-valued uncertainty assessments representing probabilities for, against, and don't know about formal assertions of interest. The proposed algorithm targets the distribution of a class of random convex polytopes which encapsulate the DS inference. The sam...
• A Semiparametric Kernel Independence Test With Application to Mutational Signatures AI翻译
  作者：Lee, DongHyuk; Zhu, Bin
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：Cancers arise owing to somatic mutations, and the characteristic combinations of somatic mutations form mutational signatures. Despite many mutational signatures being identified, mutational processes underlying a number of mutational signatures remain unknown, which hinders the identification of interventions that may reduce somatic mutation burdens and prevent the development of cancer. We demonstrate that the unknown cause of a mutational signature can be inferred by the associated signatur...
• Improved Doubly Robust Estimation in Learning Optimal Individualized Treatment Rules AI翻译
  作者：Pan, Yinghao; Zhao, Ying-Qi
  作者单位：University of North Carolina; University of North Carolina Charlotte; Fred Hutchinson Cancer Center
  摘要：Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator of the expected clinical outcome over a class of ITRs. The method enjoys two key properties. First, it is doubly robus...