2018

最新文章

• Semiparametrically efficient estimation in quantile regression of secondary analysis AI翻译
  作者：Liang, Liang; Ma, Yanyuan; Wei, Ying; Carroll, Raymond J.
  作者单位：Texas A&M University System; Texas A&M University College Station; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Columbia University; University of Technology Sydney
  摘要：Analysing secondary outcomes is a common practice for case-control studies. Traditional secondary analysis employs either completely parametric models or conditional mean regression models to link the secondary outcome to covariates. In many situations, quantile regression models complement mean-based analyses and provide alternative new insights on the associations of interest. For example, biomedical outcomes are often highly asymmetric, and median regression is more useful in describing the...
• Modelling non-stationary multivariate time series of counts via common factors AI翻译
  作者：Wang, Fangfang; Wang, Haonan
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Colorado State University System; Colorado State University Fort Collins
  摘要：We develop a new parameter-driven model for multivariate time series of counts. The time series is not necessarily stationary. We model the mean process as the product of modulating factors and unobserved stationary processes. The former characterizes the long-run movement in the data, whereas the latter is responsible for rapid fluctuations and other unknown or unavailable covariates. The unobserved stationary processes evolve independently of the past observed counts and might interact with ...
• Random networks, graphical models and exchangeability AI翻译
  作者：Lauritzen, Steffen; Rinaldo, Alessandro; Sadeghi, Kayvan
  作者单位：University of Copenhagen; Carnegie Mellon University; University of Cambridge
  摘要：We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical subgraph densities are maximum likelihood estimates of their theoretical counterparts. We then characterize all possible Markov structures for finitely exchangeable random graphs, thereby identifying a new class of Markov network models corresponding to bidirected Kneser graphs. In particular, we demonstrate that the...
• Semiparametric dynamic max-copula model for multivariate time series AI翻译
  作者：Zhao, Zifeng; Zhang, Zhengjun
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：The paper presents a novel non-linear framework for the construction of flexible multivariate dependence structure (i.e. copulas) from existing copulas based on a straightforward pairwise max-'rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared with the classical linear symmetric' mixture copula, the max-copula can be viewed as a non-linear asymmetric' framework. It is capable of modelling asymmetric dependence and joint tail behaviour while also ...
• High dimensional change point estimation via sparse projection AI翻译
  作者：Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：Change points are a very common feature of big data' that arrive in the form of a data stream. We study high dimensional time series in which, at certain time points, the mean structure changes in a sparse subset of the co-ordinates. The challenge is to borrow strength across the co-ordinates to detect smaller changes than could be observed in any individual component series. We propose a two-stage procedure called inspect for estimation of the change points: first, we argue that a good projec...
• Panning for gold: model-X' knockoffs for high dimensional controlled variable selection AI翻译
  作者：Candes, Emmanuel; Fan, Yingying; Janson, Lucas; Lv, Jinchi
  作者单位：Stanford University; University of Southern California
  摘要：Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a non-linear fashion, such as when the response is binary. Although this modelling problem has been extensively studied, it remains unclear how to control the fraction of false discoveries effectively even in high dimensional logistic regression, not to mention general high dimensional non-linear models. To address such a practical problem, we propose a ...
• Full likelihood inference for abundance from continuous time capture-recapture data AI翻译
  作者：Liu, Yang; Liu, Yukun; Li, Pengfei; Qin, Jing
  作者单位：East China Normal University; University of Waterloo; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：Capture-recapture experiments are widely used cost-effective sampling techniques for estimating population sizes or abundances in biology, ecology, demography, epidemiology and reliability studies. For continuous time capture-recapture data, existing estimation methods are based on conditional likelihoods and an inverse weighting estimating equation. The corresponding Wald-type confidence intervals for the abundance may have severe undercoverage, and their lower limits can be below the number ...
• False discovery rate control for high dimensional networks of quantile associations conditioning on covariates AI翻译
  作者：Xie, Jichun; Li, Ruosha
  作者单位：Duke University; University of Texas System; University of Texas Health Science Center Houston
  摘要：Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously the independence between on...
• Detecting and dating structural breaks in functional data without dimension reduction AI翻译
  作者：Aue, Alexander; Rice, Gregory; Sonmez, Ozan
  作者单位：University of California System; University of California Davis; University of Waterloo
  摘要：Methodology is proposed to uncover structural breaks in functional data that is fully functional' in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight that the fully functional pr...
• Unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data AI翻译
  作者：Wang, Honglang; Zhong, Ping-Shou; Cui, Yuehua; Li, Yehua
  作者单位：Purdue University System; Purdue University; Purdue University in Indianapolis; Michigan State University; Iowa State University
  摘要：We consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points. We propose test procedures based on the empirical likelihood with bias-corrected estimating equations to conduct both pointwise and simultaneous inferences. The asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses, where sparse and dense ...