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Selection of covariance patterns for longitudinal data in semi-parametric models
1 Department of Statistics and Applied Probability, National University of Singapore and Duke-NUS Graduate Medical School, Singapore
* To whom correspondence should be addressed.
The use of patterned covariance structures in the parametric analysis of longitudinal data is both elegant and efficient. However, this strategy has not been well studied for semi-parametric models for analysing such data. We propose to estimate the covariance matrix in the semi-parametric model by rearranging the non-parametric component as a profiled linear function of the data and using a local smoothing technique. This results in a parametric regression formulation that enables us to construct likelihood functions and use various information criteria to select the best fitting covariance matrix. We apply our method to reanalyse data from a two-armed clinical trial for Scleroderma patients and show our method is more efficient for estimating the parametric components in the semi-parametric model.
First published on January 19, 2009 |
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