Dynamic Mixed Models for Familial Longitudinal Data provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. It uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equicorrelation models.
The book provides a clear direction for accurate familial and longitudinal data analysis by presenting differences between the familial and longitudinal correlation models and deals with non-stationary longitudinal correlations caused by time dependent covariates. Dynamic Mixed Models for Familial Longitudinal Data offers an appropriate level of theoretical detail along with easy and interesting illustrations of real life data analysis.
Published as: Sutradhar, B. Dynamic Mixed Models for Familial Longitudinal Data. New York, Springer, 2011.