492期

最新文章

• Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data AI翻译
  作者：Xue, Lan; Qu, Annie; Zhou, Jianhui
  作者单位：Oregon State University; University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia
  摘要：We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified likelihood function for the generalized additive model, because the outcomes could be nonnormal and discrete, which makes estimation and model select...
• Multiple Change-Point Estimation With a Total Variation Penalty AI翻译
  作者：Harchaoui, Z.; Levy-Leduc, C.
  作者单位：Inria; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris
  摘要：We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise. Our approach consists in reframing this task in a variable selection context. We use a penalized least-square criterion with a L-1-type penalty for this purpose. We explain how to implement this method in practice by using the LARS/LASSO algorithm. We then prove that, in an appropriate asymptotic framework, this method provides consis...
• Modeling Competing Infectious Pathogens From a Bayesian Perspective: Application to Influenza Studies With Incomplete Laboratory Results AI翻译
  作者：Yang, Yang; Halloran, M. Elizabeth; Daniels, Michael J.; Longini, Ira M., Jr.; Burke, Donald S.; Cummings, Derek A. T.
  作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle; State University System of Florida; University of Florida; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Johns Hopkins University
  摘要：In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often cocirculate with influenza and cause influenza-like illness (ILL) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among individuals. We propose a ...
• Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence AI翻译
  作者：Smith, Michael; Min, Aleksey; Almeida, Carlos; Czado, Claudia
  作者单位：University of Melbourne; Technical University of Munich
  摘要：Copulas have proven to be very successful tools for the flexible modeling of cross-sectional dependence. In this paper we express the dependence structure of continuous-valued time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a vine in the graphical models literature, where each copula is entitled a pair-copula. We propose a Bayesian approach for the estimation of this dependence structure for longitudinal data. Bayesian selecti...
• A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph AI翻译
  作者：Liang, Kun; Nettleton, Dan
  作者单位：Iowa State University
  摘要：Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes. We developed a method based on a hidden Markov model that takes the whole graph into account and provides coherent decisions in this structured multiple...
• Chronic Disease Prevention Research Methods and Their Reliability, With Illustrations From the Women's Health Initiative AI翻译
  作者：Prentice, Ross L.
  作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：This article reviews the status of statistical methods for chronic disease prevention research, with emphasis on the reliability of findings and on future methodological needs and opportunities. Observational studies, especially cohort studies, play a major role in disease prevention research, but depend on adequate confounding control methods for a useful interpretation. Stratification and regression methods that are commonly used to control confounding are described, and comparative findings...
• Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning AI翻译
  作者：Lele, Subhash R.; Nadeem, Khurram; Schmuland, Byron
  作者单位：University of Alberta
  摘要：Maximum likelihood estimation for Generalized Linear Mixed Models (GLMM), an important class of statistical models with substantial applications in epidemiology, medical statistics, and many other fields, poses significant computational difficulties. In this article, we use data cloning, a simple computational method that exploits advances in Bayesian computation, in particular the Markov Chain Monte Carlo method, to obtain maximum likelihood estimators of the parameters in these models. This ...
• Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions AI翻译
  作者：Radchenko, Peter; James, Gareth M.
  作者单位：University of Southern California
  摘要：Numerous penalization based methods have been proposed for fitting a traditional linear regression model in which the number of predictors, p, is large relative to the number of observations, n. Most of these approaches assume sparsity in the underlying coefficients and perform some form of variable selection. Recently, some of this work has been extended to nonlinear additive regression models. However, in many contexts one wishes to allow for the possibility of interactions among the predict...
• Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime AI翻译
  作者：Taddy, Matthew A.
  作者单位：University of Chicago
  摘要：This article develops a set of tools for smoothing and prediction with dependent point event patterns. The methodology is motivated by the problem of tracking weekly maps of violent crime events, but is designed to be straightforward to adapt to a wide variety of alternative settings. In particular, a Bayesian semiparametric framework is introduced for modeling correlated time series of marked spatial Poisson processes. The likelihood is factored into two independent components: the set of tot...
• Estimating Individual-Level Risk in Spatial Epidemiology Using Spatially Aggregated Information on the Population at Risk AI翻译
  作者：Diggle, Peter J.; Guan, Yongtao; Hart, Anthony C.; Paize, Fauzia; Stanton, Michelle
  作者单位：Yale University; Lancaster University; Johns Hopkins University; Alder Hey Children's NHS Foundation Trust; University of Liverpool; University of Liverpool
  摘要：We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. We develop data-driven methods to select the weights used in the estimat...