Please use this identifier to cite or link to this item: https://doi.org/10.1177/0962280208099447
Title: Selection of covariance patterns for longitudinal data in semi-parametric models
Authors: Li, J. 
Wong, W.K.
Issue Date: Apr-2010
Citation: Li, J., Wong, W.K. (2010-04). Selection of covariance patterns for longitudinal data in semi-parametric models. Statistical Methods in Medical Research 19 (2) : 183-196. ScholarBank@NUS Repository. https://doi.org/10.1177/0962280208099447
Abstract: 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. © 2010 The Author(s),Reprints and permissions.
Source Title: Statistical Methods in Medical Research
URI: http://scholarbank.nus.edu.sg/handle/10635/105349
ISSN: 09622802
DOI: 10.1177/0962280208099447
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