2020

最新文章

• Discussion of A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression AI翻译
  作者：Li, Xiudi; Shojaie, Ali
  作者单位：University of Washington; University of Washington Seattle
• High-Dimensional Precision Medicine From Patient-Derived Xenografts AI翻译
  作者：Rashid, Naim U.; Luckett, Daniel J.; Chen, Jingxiang; Lawson, Michael T.; Wang, Longshaokan; Zhang, Yunshu; Laber, Eric B.; Liu, Yufeng; Yeh, Jen Jen; Zeng, Donglin; Kosorok, Michael R.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University
  摘要：The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit t...
• Bayesian Model Search for Nonstationary Periodic Time Series AI翻译
  作者：Hadj-Amar, Beniamino; Rand, Barbel Finkenstadt; Fiecas, Mark; Levi, Francis; Huckstepp, Robert
  作者单位：University of Warwick; University of Minnesota System; University of Minnesota Twin Cities; University of Warwick; University of Warwick
  摘要：We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo algorithm that simultaneously updates the change-points and the periodici...
• L2RM: Low-Rank Linear Regression Models for High-Dimensional Matrix Responses AI翻译
  作者：Kong, Dehan; An, Baiguo; Zhang, Jingwen; Zhu, Hongtu
  作者单位：University of Toronto; Capital University of Economics & Business; University of North Carolina; University of North Carolina Chapel Hill
  摘要：The aim of this article is to develop a low-rank linear regression model to correlate a high-dimensional response matrix with a high-dimensional vector of covariates when coefficient matrices have low-rank structures. We propose a fast and efficient screening procedure based on the spectral norm of each coefficient matrix to deal with the case when the number of covariates is extremely large. We develop an efficient estimation procedure based on the trace norm regularization, which explicitly ...
• Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials AI翻译
  作者：Bebu, Ionut
  作者单位：George Washington University
• GAP: A General Framework for Information Pooling in Two-Sample Sparse Inference AI翻译
  作者：Xia, Yin; Cai, T. Tony; Sun, Wenguang
  作者单位：Fudan University; University of Pennsylvania; University of Southern California
  摘要：This article develops a general framework for exploiting the sparsity information in two-sample multiple testing problems. We propose to first construct a covariate sequence, in addition to the usual primary test statistics, to capture the sparsity structure, and then incorporate the auxiliary covariates in inference via a three-step algorithm consisting of grouping, adjusting and pooling (GAP). The GAP procedure provides a simple and effective framework for information pooling. An important a...
• On Degrees of Freedom of Projection Estimators With Applications to Multivariate Nonparametric Regression AI翻译
  作者：Chen, Xi; Lin, Qihang; Sen, Bodhisattva
  作者单位：New York University; University of Iowa; Columbia University
  摘要：In this article, we consider the nonparametric regression problem with multivariate predictors. We provide a characterization of the degrees of freedom and divergence for estimators of the unknown regression function, which are obtained as outputs of linearly constrained quadratic optimization procedures; namely, minimizers of the least-squares criterion with linear constraints and/or quadratic penalties. As special cases of our results, we derive explicit expressions for the degrees of freedo...
• Rejoinder AI翻译
  作者：Efron, Bradley
• Revealing Subgroup Structure in Ranked Data Using a Bayesian WAND AI翻译
  作者：Johnson, S. R.; Henderson, D. A.; Boys, R. J.
  作者单位：Newcastle University - UK
  摘要：Ranked data arise in many areas of application ranging from the ranking of up-regulated genes for cancer to the ranking of academic statistics journals. Complications can arise when rankers do not report a full ranking of all entities; for example, they might only report their top-M ranked entities after seeing some or all entities. It can also be useful to know whether rankers are equally informative, and whether some entities are effectively judged to be exchangeable. Revealing subgroup stru...
• Efficiently Inferring the Demographic History of Many Populations With Allele Count Data AI翻译
  作者：Kamm, Jack; Terhorst, Jonathan; Durbin, Richard; Song, Yun S.
  作者单位：Wellcome Trust Sanger Institute; University of Cambridge; University of Michigan System; University of Michigan; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Chan Zuckerberg Initiative (CZI)
  摘要：The sample frequency spectrum (SFS), or histogram of allele counts, is an important summary statistic in evolutionary biology, and is often used to infer the history of population size changes, migrations, and other demographic events affecting a set of populations. The expected multipopulation SFS under a given demographic model can be efficiently computed when the populations in the model are related by a tree, scaling to hundreds of populations. Admixture, back-migration, and introgression ...