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1:00 pm Registration (28 attended)
1:30 pm Invited Speaker: Debajyoti Sinha
Medical University of South Carolina
'Analysis of Panel Count Data: A Bayesian Approach'
We consider models and analysis of panel count data when the termination time
for each subject may depend on the history of the recurrent events.
We propose a fully specified semiparametric model for the joint
distribution of the recurrent events and the termination time. We
provide a natural motivation and interpretation of the model and
derive its several novel properties.
We develop efficient Markov chain Monte Carlo algorithms
for sampling from the posterior distribution
of the parameters.
Comparisons
are made to the existing models and methods for panel count data.
We demonstrate the usefulness of our new
models and methodologies through
the reanalysis of a dataset from a clinical
trial.
2:45 pm Student Paper Competition:
(Select the title of the talk to view the abstract)
4:15 pm Invited Speaker: Steven Liu
Department of Educational Psychology
University of South Carolina
'An Introduction to Hierarchical Linear Modeling'
Researchers often use hierarchical linear models (HLM) to analyze
data that involves individuals nested in social settings. In
school-based studies students in the same school share similar
experiences and tend to produce correlated observations, which violate
the independence assumption of a regular linear model. HLM offers a
flexible approach to handle such nested data. The parameterization of
HLM bears some resemblance to Bayesian linear models in which the
regression coefficients in the regular linear models are random variables
themselves. The presentation will use growth modeling and missing data
analysis to illustrate some of HLM applications.
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