Analysis of
Hierarchical Biomedical Data Using
Semiparametric Models Naisyin Wang
Statistics Department Texas A&M University Abstract
The analysis of hierarchical biomedical data
makes standard parametric statistical modeling tasks difficult. In
certain applications, knowledge regarding the variation embedded in
different levels of hierarchies, as well as the information regarding
central locations, are of interest. In this talk, I will describe
a simple semiparametric approach that allows us to model both the first
and second moments in hierarchical data. In particular, the method
enable us to reduce estimation variation of the first moment through
accounting for correlations in the data. It also enable us to
obtain a simple covariance structure when simplification can be
achieved. I will use data from on-going Biomedical studies to
illustrate the main points of the modeling strategy.
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