Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration [0.03%]
生物信息学中的统计研究：设计、建模、结构学习及数据整合
Jeffrey S Morris,Veerabhadran Baladandayuthapani
Jeffrey S Morris
The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analys...
Comparison and Contrast of Two General Functional Regression Modeling Frameworks [0.03%]
两种泛函回归模型框架的比较与探讨
Jeffrey S Morris
Jeffrey S Morris
In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling framework. Over the past number of years, my collaborators and I have als...
Madan G Kundu,Jaroslaw Harezlak,Timothy W Randolph
Madan G Kundu
This article addresses estimation in regression models for longitudinally-collected functional covariates (time-varying predictor curves) with a longitudinal scaler outcome. The framework consists of estimating a time-varying coefficient fu...
Bryan E Shepherd,Qi Liu
Bryan E Shepherd
Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols [0.03%]
采用因子载荷非恒定的潜变量模型分析二次无机气溶胶相关的细颗粒物成分
Zhenzhen Zhang,Marie S O&#x;Neill,Brisa N Sánchez
Zhenzhen Zhang
Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protecti...
Jonathan E Gellar,Elizabeth Colantuoni,Dale M Needham et al.
Jonathan E Gellar et al.
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects p...
Robust estimation of marginal regression parameters in clustered data [0.03%]
聚簇数据分析中边缘回归参数的稳健估计方法研究
Somnath Datta,James D Beck
Somnath Datta
We develop robust methods for analyzing clustered data where estimation of marginal regression parameters is of interest. Inverse cluster size reweighting in the objective function to be minimized is incorporated to handle the issue of info...
Applications of a Kullback-Leibler Divergence for Comparing Non-nested Models [0.03%]
应用Kullback-Leibler散度比较非嵌套模型
Chen-Pin Wang,Booil Jo
Chen-Pin Wang
Wang and Ghosh (2011) proposed a Kullback-Leibler divergence (KLD) which is asymptotically equivalent to the KLD by Goutis and Robert (1998) when the reference model (in comparison with a competing fitted model) is correctly specified and w...
Bo Cai,Andrew B Lawson,Md Monir Hossain et al.
Bo Cai et al.
Spatial-temporal data requires flexible regression models which can model the dependence of responses on space- and time-dependent covariates. In this paper, we describe a semiparametric space-time model from a Bayesian perspective. Nonline...
Bayesian latent variable models for spatially correlated tooth-level binary data in caries research [0.03%]
用于龋齿研究的具有空间相关性的牙齿二元数据的贝叶斯潜在变量模型
Y Zhang,D Todem,K Kim et al.
Y Zhang et al.
Analysis of dental caries is traditionally based on aggregated scores, which are summaries of caries experience for each individual. A well-known example of such scores is the decayed, missing and filled teeth or tooth surfaces index introd...