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:: Volume 18, Issue 1 (8-2024) ::
JSS 2024, 18(1): 0-0 Back to browse issues page
Shrinkage Estimators in Semi-Parametric Heteroscedastic Hierarchical Models with Restricted Joint Empirical likelihood
Seyed Kamran Ghoreishi *
Abstract:   (1040 Views)
In this paper, we first introduce semi-parametric heteroscedastic hierarchical models. Then, we define a new version of the empirical likelihood function (Restricted Joint Empirical likelihood) and use it to obtain the shrinkage estimators of the models' parameters in these models. Under different assumptions, a simulation study investigates the better performance of the restricted joint empirical likelihood function in the analysis of semi-parametric heterogeneity hierarchical models. Furthermore, we analyze an actual data set using the RJEL method.
Keywords: Moment estimator, Stein unbiased risk estimator, Estimating equations, Empirical maximum likelihood estimator
Full-Text [PDF 238 kb]   (639 Downloads)    
Type of Study: Research | Subject: Theoritical Statistics
Received: 2023/06/16 | Accepted: 2024/08/31 | Published: 2024/06/4
References
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Ghoreishi S K. Shrinkage Estimators in Semi-Parametric Heteroscedastic Hierarchical Models with Restricted Joint Empirical likelihood. JSS 2024; 18 (1)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 18, Issue 1 (8-2024) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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