Statistics and Data Science Seminar
Prof. Annie Qu
UIUC
Model Selection of Correlation Structure for Clustered Data
Abstract: Model selection of correlation structure is a challenging problem because it
involves a higher order of moments than model selection of covariates only.
In addition, the high dimension of the correlation parameters could make the
estimation of those parameters unreliable since the number of repeated
measurements might be relatively small compared to the dimension of the
correlation parameters. However, the correct specification of the correlation
structure plays an important role in improving estimation efficiency for
clustered data. We propose to select the correlation structure for clustered data
from a number of candidate structures through a group-wise basis matrices
selection strategy. The proposed method has the advantages of not requiring
the likelihood function and of being computationally efficient. Also, the method
can identify complex correlation structures. Furthermore, it is applicable for
both continuous and discrete response data. In theory, we show that the
proposed method enjoys the oracle property of selecting the true correlation
structure consistently and estimating the correlation parameters with the same
asymptotic normal distribution as if the true structure is known. This is joint
work with Jianhui Zhou of University of Virginia.
Wednesday March 17, 2010 at 3:00 PM in SEO 636