In this paper, we first define longitudinal-dynamic heteroscedastic hierarchical normal models. These models can be used to fit longitudinal data in which the dependency structure is constructed through a dynamic model rather than observations. We discuss different methods for estimating the hyper-parameters. Then the corresponding estimates for the hyper-parameter that causes the association in the model will be presented. The comparison among various empirical estimators is illustrated through a simulation study. Finally, we apply our methods to a real dataset.
Ghoreishi S K. Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models. Andishe 2021; 25 (2) :113-120 URL: http://andisheyeamari.irstat.ir/article-1-841-en.html