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:: Volume 19, Issue 1 (9-2025) ::
JSS 2025, 19(1): 1-28 Back to browse issues page
Selection in Small Area Estimation under AR-GARCH Models Based on the Gradient Boosting Algorithm
Om-aulbanin Bashiri Goudarzi , Abdolreza Sayyareh * , Sedigheh Zamani Mehreyan
Abstract:   (181 Views)
The boosting algorithm is a hybrid algorithm to reduce variance, a family of machine learning algorithms in supervised learning. This algorithm is a method to transform weak learning systems into strong systems based on the combination of different results. In this paper, mixture models with random effects are considered for small areas, where the errors follow the AR-GARCH model. To select the variable, machine learning algorithms, such as boosting algorithms, have been proposed. Using simulated and tax liability data, the boosting algorithm's performance is studied and compared with classical variable selection methods, such as the step-by-step method.
Keywords: Boosting algorithm‎, ‎variable selection‎, ‎small area estimation‎, ‎$AR-GARCH$ model
Full-Text [PDF 439 kb]   (125 Downloads)    
Type of Study: Research | Subject: Statistical Inference
Received: 2024/04/19 | Accepted: 2025/09/1 | Published: 2025/05/18
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Bashiri Goudarzi O, Sayyareh A, Zamani Mehreyan S. Selection in Small Area Estimation under AR-GARCH Models Based on the Gradient Boosting Algorithm. JSS 2025; 19 (1) :1-28
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 19, Issue 1 (9-2025) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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