2014

最新文章

• Variable-Domain Functional Regression for Modeling ICU Data AI翻译
  作者：Gellar, Jonathan E.; Colantuoni, Elizabeth; Needham, Dale M.; Crainiceanu, Ciprian M.
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine
  摘要：We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registratio...
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models AI翻译
  作者：Fan, Jianqing; Ma, Yunbei; Dai, Wei
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Princeton University; Southwestern University of Finance & Economics - China
  摘要：The varying coefficient model is an important class of nonparametric statistical model, which allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this article, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high-dimensional varying coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ...
• Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall's Tau AI翻译
  作者：Jiang, Yuan; Li, Ni; Zhang, Heping
  作者单位：Oregon State University; Hainan Normal University; Yale University; Yale University; Sun Yat Sen University
  摘要：Identifying replicable genetic variants for addiction has been extremely challenging. Besides the common difficulties with genome-wide association studies (GWAS), environmental factors are known to be critical to addiction, and comorbidity is widely observed. Despite the importance of environmental factors and comorbidity for addiction study, few GWAS analyses adequately considered them due to the limitations of the existing statistical methods. Although parametric methods have been developed ...
• Multivariate Analysis of Longitudinal Ordinal Data With Mixed Effects Models, With Application to Clinical Outcomes in Osteoarthritis AI翻译
  作者：Laffont, Celine Marielle; Vandemeulebroecke, Marc; Concordet, Didier
  作者单位：INRAE; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Universite de Toulouse; Ecole Nationale Veterinaire de Toulouse; Universite Toulouse III - Paul Sabatier; Universite Federale Toulouse Midi-Pyrenees (ComUE); Institut National Polytechnique de Toulouse; Novartis
  摘要：Our objective was to evaluate the efficacy of robenacoxib in osteoarthritic dogs using four ordinal responses measured repeatedly over time. We propose a multivariate probit mixed effects model to describe the joint evolution of endpoints and to evidence the intrinsic correlations between responses that are not due to treatment effect. Maximum likelihood computation is intractable within reasonable time frames. We therefore use a pairwise modeling approach in combination with a stochastic EM a...
• Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods AI翻译
  作者：Stefanski, L. A.; Wu, Yichao; White, Kyle
  作者单位：North Carolina State University
  摘要：Using the relationships among ridge regression, LASSO estimation, and measurement error attenuation as motivation, a new measurement-error-model-based approach to variable selection is developed. After describing the approach in the familiar context of linear regression, we apply it to the problem of variable selection in nonparametric classification, resulting in a new kernel-based classifier with LASSO-like shrinkage and variable-selection properties. Finite-sample performance of the new cla...
• Dimension-Reduced Modeling of Spatio-Temporal Processes AI翻译
  作者：Brynjarsdottir, Jenny; Berliner, L. Mark
  作者单位：University System of Ohio; Case Western Reserve University; University System of Ohio; Ohio State University
  摘要：The field of spatial and spatio-temporal statistics is increasingly faced with the challenge of very large datasets. The classical approach to spatial and spatio-temporal modeling is very computationally demanding when datasets are large, which has led to interest in methods that use dimension-reduction techniques. In this article, we focus on modeling of two spatio-temporal processes where the primary goal is to predict one process from the other and where datasets for both processes are larg...
• Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging AI翻译
  作者：Rizopoulos, Dimitris; Hatfield, Laura A.; Carlin, Bradley P.; Takkenberg, Johanna J. M.
  作者单位：Erasmus University Rotterdam; Erasmus MC; Harvard University; University of Minnesota System; University of Minnesota Twin Cities; Erasmus University Rotterdam; Erasmus MC
  摘要：The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in recent years. More recently, a new and attractive application of this type of model has been to obtain individualized predictions of survival probabilities and/or of future longitudinal responses. The advantageous feature of these predictions is that they are dynamically updated as extra longitudinal responses are collected for the subjects of interest, pro...
• Detection of Unusual Increases in MRI Lesion Counts in Individual Multiple Sclerosis Patients AI翻译
  作者：Zhao, Yinshan; Li, David K. B.; Petkau, A. John; Riddehough, Andrew; Traboulsee, Anthony
  作者单位：University of British Columbia; University of British Columbia; University of British Columbia; University of British Columbia
  摘要：Data Safety and Monitoring Boards (DSMBs) for multiple sclerosis clinical trials consider an increase of contrast-enhancing lesions on repeated magnetic resonance imaging an indicator for potential adverse events. However, there are no published studies that clearly identify what should be considered an unexpected increase of lesion activity for a patient. To address this problem, we consider as an index the likelihood of observing lesion counts as large as those observed on the recent scans o...
• Exact Meta-Analysis Approach for Discrete Data and its Application to 2 x 2 Tables With Rare Events AI翻译
  作者：Liu, Dungang; Liu, Regina Y.; Xie, Min-ge
  作者单位：University System of Ohio; University of Cincinnati; Rutgers University System; Rutgers University New Brunswick
  摘要：This article proposes a general exact meta-analysis approach for synthesizing inferences from multiple studies of discrete data. The approach combines the p-value functions (also known as significance functions) associated with the exact tests from individual studies. It encompasses a broad class of exact meta-analysis methods, as it permits broad choices for the combining elements, such as tests used in individual studies, and any parameter of interest. The approach yields statements that exp...
• Filtering With Heavy Tails AI翻译
  作者：Harvey, Andrew; Luati, Alessandra
  作者单位：University of Cambridge; University of Bologna
  摘要：An unobserved components model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation-driven model, based on a conditional Student's t-distribution, which is tractable and retains some of the desirable features of the linear Gaussian model. Letting the dynamics be driven by the score of the conditional distribution leads to a specification that is not only easy to implement, but...