:: Volume 25, Issue 2 (3-2021) ::
Andishe 2021, 25(2): 113-120 Back to browse issues page
Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models
Seyed Kamran Ghoreishi *
Abstract:   (485 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.
Keywords: Dynamic models, Heteroscedastic, Hierarchical models, Longitudinal data, Shrinkage estimators, Stein's unbiased estimator.
Full-Text [PDF 234 kb]   (147 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/02/1 | Accepted: 2021/03/17 | Published: 2021/03/18


XML   Persian Abstract   Print



Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 25, Issue 2 (3-2021) Back to browse issues page