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Bayesian inference on dependence in multivariate longitudinal data
Cholesky decomposition covariance matrix moment-matching oxidative stress random effects shrinkage prior.
2012/9/17
In many applications, it is of interest to assess the dependence structure in multivariate longitudinal data. Discovering such dependence is challenging
due to the dimensionality involved. By concate...
Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data
composite likelihood EM algorithm latent Markov model pairwise likelihood
2012/9/17
In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and ...
HIV dynamics and natural history studies: Joint modeling with doubly interval-censored event time and infrequent longitudinal data
AIDS antiviral treatment interval censoring semiparametric regression
2011/6/16
Hepatitis C virus (HCV) coinfection has become one of the most
challenging clinical situations to manage in HIV-infected patients.
Recently the effect of HCV coinfection on HIV dynamics following
i...
Covariate adjusted functional principal components analysis for longitudinal data
Functional data analysis functional principal componentsanalysis local linear regression longitudinal data analysis smoothing sparse data
2010/3/11
Classical multivariate principal component analysis has been extended
to functional data and termed functional principal component
analysis (FPCA). Most existing FPCA approaches do not accommodate
...
Longitudinal Data with Follow-up Truncated by Death:Match the Analysis Method to Research
Censoring generalized estimating equations longitudinal data missing data quality of life random effects models truncation by death
2010/3/9
Diverse analysis approaches have been proposed to distin-
guish data missing due to death from nonresponse, and to summarize
trajectories of longitudinal data truncated by death. We demonstrate
how...
Explicit connections between longitudinal data analysis and kernel machines
Best linear unbiased prediction classification generalized linear mixed models machine learning linear mixed models reproducing kernel Hilbert spaces
2009/9/16
Two areas of research – longitudinal data analysis and kernel machines – have large, but mostly distinct, literatures. This article shows explicitly that both fields have much in common with each othe...
INFERRING GENE DEPENDENCY NETWORKS FROM GENOMIC LONGITUDINAL DATA：A FUNCTIONAL DATA APPROACH
graphical model longitudinal data dynamical correlation gene dependency networks
2009/2/25
A key aim of systems biology is to unravel the regulatory interactions among genes
and gene products in a cell. Here we investigate a graphical model that treats the
observed gene expression over ti...
Bayesian joint modelling of the mean and covariance structures for normal longitudinal data
Antedependence models Bayes estimation Fisher scoring Gibbs sampling
2009/2/23
We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We ...