2017

最新文章

• Network Reconstruction From High-Dimensional Ordinary Differential Equations AI翻译
  作者：Chen, Shizhe; Shojaie, Ali; Witten, Daniela M.
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：We consider the task of learning a dynamical systemfromhigh-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. Wemodel the dynamical system nonparametrically as a systemof additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient...
• Partition MCMC for Inference on Acyclic Digraphs AI翻译
  作者：Kuipers, Jack; Moffa, Giusi
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of London; University College London
  摘要：Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain. Structure learning forms one of the inference challenges of statistical graphical models. Markov chain Monte Carlo (MCMC) methods, notably structure MCMC, to sample graphs from the posterior distribution given the data are probably the only viable option f...
• Weighted Statistic in Detecting Faint and Sparse Alternatives for High-Dimensional Covariance Matrices AI翻译
  作者：Yang, Qing; Pan, Guangming
  作者单位：Nanyang Technological University
  摘要：This article considers testing equality of two population covariance matrices when the data dimension p diverges with the sample size n (p/n c > 0). We propose a weighted test statistic that is data-driven and powerful in both faint alternatives (many small disturbances) and sparse alternatives (several large disturbances). Its asymptotic null distribution is derived by large random matrix theory without assuming the existence of a limiting cumulative distribution function of the population co...
• Covariance Regression Analysis AI翻译
  作者：Zou, Tao; Lan, Wei; Wang, Hansheng; Tsai, Chih-Ling
  作者单位：Peking University; Australian National University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of California System; University of California Davis
  摘要：This article introduces covariance regression analysis for a p-dimensional response vector. The proposed method explores the regression relationship between the p-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and lar...
• Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer AI翻译
  作者：Egleston, Brian L.; Uzzo, Robert G.; Wong, Yu-Ning
  作者单位：Fox Chase Cancer Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Fox Chase Cancer Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Fox Chase Cancer Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：Rates of kidney cancer have been increasing, with small incidental tumors experiencing the fastest growth rates. Much of the increase could be due to increased use of CT scans, MRIs, and ultrasounds for unrelated conditions. Many tumors might never have been detected or become symptomatic in the past. This suggests that many patients might benefit from less aggressive therapy, such as active surveillance by which tumors are surgically removed only if they become sufficiently large. However, it...
• Hierarchical Latin Hypercube Sampling AI翻译
  作者：Garg, Vikram V.; Stogner, Roy H.
  作者单位：Massachusetts Institute of Technology (MIT); University of Texas System; University of Texas Austin
  摘要：Latin hypercube sampling (LHS) is a robust, scalable Monte Carlo method that is used in many areas of science and engineering. We present a new algorithm for generating hierarchic Latin hypercube sets (HLHS) that are recursively divisible into LHS subsets. Based on this new construction, we introduce a hierarchical incremental LHS (HILHS) method that allows the user to employ LHS in a flexibly incremental setting. This overcomes a drawback of many LHS schemes that require the entire sample set...
• Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies AI翻译
  作者：Fogarty, Colin B.; Shi, Pixu; Mikkelsen, Mark E.; Small, Dylan S.
  作者单位：Massachusetts Institute of Technology (MIT); University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer...
• A Concave Pairwise Fusion Approach to Subgroup Analysis AI翻译
  作者：Ma, Shujie; Huang, Jian
  作者单位：University of California System; University of California Riverside; University of Iowa
  摘要：An important step in developing individualized treatment strategies is correct identification of subgroups of a heterogeneous population to allow specific treatment for each subgroup. This article considers the problem using samples drawn from a population consisting of subgroups with different mean values, along with certain covariates. We propose a penalized approach for subgroup analysis based on a regression model, in which heterogeneity is driven by unobserved latent factors and thus can ...
• Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data AI翻译
  作者：Manrique-Vallier, Daniel; Reiter, Jerome P.
  作者单位：Indiana University System; Indiana University Bloomington; Duke University
  摘要：In categorical data, it is typically the case that some combinations of variables are theoretically impossible, such as a 3-year-old child who is married or a man who is pregnant. In practice, however, reported values often include such structural zeros due to, for example, respondent mistakes or data processing errors. To purge data of such errors, many statistical organizations use a process known as edit-imputation. The basic idea is first to select reported values to change according to so...
• Quantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program AI翻译
  作者：Miles, Caleb H.; Shpitser, Ilya; Kanki, Phyllis; Meloni, Seema; Tchetgen, Eric J. Tchetgen
  作者单位：University of California System; University of California Berkeley; Johns Hopkins University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Since the early 2000s, evidence has accumulated for a significant differential effect of first-line antiretroviral therapy (ART) regimens on human immunodeficiency virus (HIV) viral load suppression. This finding was replicated in our data from the Harvard President's Emergency Plan for AIDS Relief (PEPFAR) program in Nigeria. Investigators were interested in finding the source of these differences, that is, understanding the mechanisms through which one regimen outperforms another, particular...