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Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models
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Seyed Kamran Ghoreishi *   |
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Abstract: (2996 Views) |
| 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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| Keywords: Dynamic models, Heteroscedastic, Hierarchical models, Longitudinal data, Shrinkage estimators, Stein's unbiased estimator. |
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Full-Text [PDF 234 kb]
(1240 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2021/02/1 | Accepted: 2021/03/17 | Published: 2021/03/18
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