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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 ...
Controlling for individual heterogeneity in longitudinal models, with applications to student achievement
longitudinal data analysis random effects models fixed effects models omitted variables
2009/9/16
Longitudinal data tracking repeated measurements on individuals are highly valued for research because they offer controls for unmeasured individual heterogeneity that might otherwise bias results. Ra...
Causal inference in longitudinal studies with history-restricted marginal structural models
causal inference counterfactual marginal structural model longitudinal study IPTW G-computation Double Robust
2009/9/16
A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be bette...
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...