Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models
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Abstract:
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.
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Journal title
volume 25 issue 2
pages 113- 120
publication date 2021-03
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